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.

python/README.md

@@ -101,3 +101,52 @@ print(results)
 ### Development
 
 See [CONTRIBUTING.md](./CONTRIBUTING.md) for information on how to contribute to LanceDB.
+
+### Opening SQL blob descriptors
+
+A SQL `blob_descriptor(video)` result keeps the Blob V2 storage metadata and a
+`source` struct containing its table URI, version, commit timestamp, branch,
+column and row identity. This example uses a remote SQL connection whose SQL
+service produces those source-bearing descriptors. Open the reference on the
+source table to read incrementally:
+
+```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"])
+```
+
+`open_blob_file` accepts the dictionary or an Arrow struct scalar directly. It
+keeps the query's version and branch without changing the table handle's checkout.
+Payload bytes load on reads. An external blob with an unspecified size may need a
+metadata-only HEAD request to open.
+
+`videos.open_blob_files(rows["video"])` returns an iterator over the same handles,
+preserving order, duplicates and nulls (`None`). It accepts Arrow arrays/chunked
+arrays or another iterable and opens each handle as iteration advances. Close each
+non-null handle after use. Async tables offer `await videos.open_blob_file(...)`
+and `async for video in videos.open_blob_files(...)`.
+
+```python
+for video in videos.open_blob_files(rows["video"]):
+    if video is not None:
+        with video:
+            header = video.read(4096)
+```
+
+To extract a frame while retaining each row's timestamp, iterate over the
+handles and rows together:
+
+```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"])
+```