feat(python): open lazy blob files from SQL descriptors (#4668)

SQL blob descriptors need pinned source identity to reopen a value after
the query finishes. Adds `open_blob_file(descriptor)` and
`open_blob_files(descriptors)` so Python can read the original blob
incrementally without materializing it in the SQL result.

### Python experience

```python
rows = db.execute_query("""
    SELECT f.timestamp, blob_descriptor(v.video) AS video
    FROM videos v JOIN frames f ON v.id = f.video_id
""").read_all()

videos = db.open_table("videos")
for row in rows.to_pylist():
    if row["video"] is not None:
        with videos.open_blob_file(row["video"]) as video:
            frame = extract_frame(video, row["timestamp"])
```

The singular API accepts the descriptor dictionary or an Arrow struct
scalar directly and returns a seekable `BlobFile`.
`videos.open_blob_files(rows["video"])` accepts an Arrow array/chunked
array or another iterable, opens handles as iteration advances, and
preserves order, duplicates, and nulls as `None`. Async tables provide
`await videos.open_blob_file(...)` and `async for video in
videos.open_blob_files(...)`.

The iterable API can be used directly with the Arrow result column. Zip
it with the rows to keep each frame timestamp:

```python
for row, video in zip(rows.to_pylist(), videos.open_blob_files(rows["video"])):
    if video is not None:
        with video:
            frame = extract_frame(video, row["timestamp"])
```

Each iteration opens one handle. Null descriptors yield `None`; handles
preserve input order and duplicates, and the `with` block closes each
non-null file.

For an async table:

```python
async for video in async_videos.open_blob_files(rows["video"]):
    if video is not None:
        with video:
            payload = await video.aread()
```

### Execution

The descriptor retains the canonical `kind`, `position`, `size`,
`blob_id`, and `blob_uri` fields and carries a `source` struct with its
dataset URI, version, commit timestamp, branch, column, row reference,
and whether that reference is a physical address. Native opening
resolves that exact snapshot without changing the table handle's
checkout and rejects a descriptor belonging to another table. Remote
handles send authenticated byte-range requests with the captured
version/branch, commit timestamp, and source URI, so an old physical
address is never interpreted against the latest table.

Known-size remote descriptors open without a payload request. External
blobs with an unspecified size use a metadata-only HEAD request. Payload
bytes load only when the file is read; full reads still allocate the
full value. Existing row-ID-based `fetch_blob_files` remains available
when a descriptor is not already present.

Coverage includes native snapshot preservation after overwrite, both row
IDs and addresses, dictionary/Arrow inputs, null/empty values, reordered
and duplicate descriptors, lazy iterable behavior, async iteration, and
remote range pinning for all six storage kinds.

Descriptor reads isolate their pinned snapshot from the live table
handle's freshness state. Both native and remote reads verify the source
commit timestamp to reject a replaced snapshot after table recreation.

docs/src/python/python.md

@@ -538,12 +538,64 @@ instead of being materialized with the rest of the row.
 
 `lancedb.BlobType` is `lance.blob.BlobType` when pylance is installed. Without
 pylance, LanceDB uses a matching `lance.blob.v2` extension type so blob columns
-still work. Queries return descriptors. Call
+still work. Native table queries return descriptors. Call
 [`fetch_blob_files`][lancedb.table.Table.fetch_blob_files] for lazy reads or
 [`fetch_blobs`][lancedb.table.Table.fetch_blobs] for eager bytes.
 One missing or deleted row ID rejects the entire blob-read batch; the APIs do
 not return partial results.
 
+Remote SQL returns blob bytes by default. Select `blob_descriptor(video)` to
+return an openable reference instead, then use
+[`open_blob_file`][lancedb.table.Table.open_blob_file] or the lazy iterable
+[`open_blob_files`][lancedb.table.Table.open_blob_files]:
+
+```python
+rows = db.execute_query("""
+    SELECT f.timestamp, blob_descriptor(v.video) AS video
+    FROM videos v JOIN frames f ON v.id = f.video_id
+""").read_all()
+videos = db.open_table("videos")
+for row in rows.to_pylist():
+    if row["video"] is not None:
+        with videos.open_blob_file(row["video"]) as video:
+            frame = extract_frame(video, row["timestamp"])
+```
+
+This example uses a remote SQL connection that produces source-bearing
+descriptors. The descriptor contains `kind`, `position`, `size`, `blob_id`,
+`blob_uri`, and a `source` struct with `uri`, `version`, `committed_at_micros`,
+`branch`, `column`, `row_reference`, and `row_is_address`. The opener verifies
+the source table and commit and reads the captured version without changing
+the table handle's checkout. Raw native storage descriptors omit `source`;
+use `fetch_blob_files(column, row_ids)` for those query results.
+
+The singular API accepts a dictionary or Arrow struct scalar. The iterable
+accepts Arrow arrays/chunked arrays or another iterable, opens handles as
+iteration advances, and preserves order, duplicates, and nulls as `None`.
+Close each non-null handle after use. Async tables provide
+`await table.open_blob_file(...)` and
+`async for file in table.open_blob_files(...)`.
+
+```python
+for row, video in zip(rows.to_pylist(), videos.open_blob_files(rows["video"])):
+    if video is not None:
+        with video:
+            frame = extract_frame(video, row["timestamp"])
+```
+
+For an async table, the iterable is consumed with `async for`:
+
+```python
+async for video in async_videos.open_blob_files(rows["video"]):
+    if video is not None:
+        with video:
+            payload = await video.aread()
+```
+
+Payload bytes load on reads. Opening an external blob with unspecified size
+can issue a metadata-only HEAD request. Remote reads use authenticated range
+requests that validate the captured URI and commit timestamp.
+
 Blobs inside lists can be written and queried as descriptors. They are omitted
 from `blob_columns()` because row ids do not identify individual list elements,
 so the fetch APIs cannot read their bytes yet.