Add object_store parameter to register/read file methods for thread-safe credentials

[qzyu999]

Which issue does this PR close?

Closes #1624.

Rationale for this change

When using datafusion-python as a compute backend from multi-threaded Python applications (e.g., PyIceberg), cloud credentials must currently be set via os.environ before calling register_parquet(). Since os.environ is process-global, concurrent threads mutating it causes credential cross-contamination, requiring a global lock that serializes all I/O operations and negates any parallelism benefit.

The existing register_object_store() method already solves thread-safety, but requires callers to manually parse bucket names from paths, determine the correct URL scheme, and make a separate call before register_parquet. This PR adds a convenience object_store parameter that handles all of this internally.

What changes are included in this PR?

1. New private helper _register_object_store_for_path(path, store) parses a URL path via urllib.parse.urlparse to extract scheme and host, then calls register_object_store() internally.

2. New object_store parameter added to all file-based register/read methods:

   • register_parquet / read_parquet
   • register_csv / read_csv
   • register_json / read_json
   • register_avro / read_avro
   • register_arrow / read_arrow

3. Test suite (python/tests/test_object_store_param.py) with 23 tests:

   • 8 unit tests for URL parsing logic (s3, gs, az, https, error cases)
   • 13 mock-based tests verifying each method correctly delegates to register_object_store
   • 2 end-to-end integration tests using LocalFileSystem with real Parquet files

All changes are purely in the Python layer no Rust code modified.

Are there any user-facing changes?

Yes. All file-based register_* and read_* methods on SessionContext now accept an optional object_store keyword argument. When provided with a pre-configured store instance (e.g., AmazonS3, GoogleCloud, MicrosoftAzure), the store is automatically registered for the URL parsed from the path.

Example usage:

from datafusion import SessionContext
from datafusion.object_store import AmazonS3

ctx = SessionContext()
store = AmazonS3(bucket_name='my-bucket', region='us-east-1',
                 access_key_id=key, secret_access_key=secret)
ctx.register_parquet('my_table', 's3://my-bucket/data.parquet', object_store=store)

This is a backward-compatible addition (the parameter defaults to None and existing call sites are unaffected).

[qzyu999]

Hi @timsaucer, this PR would help improve the efficiency of some operations related apache/iceberg-python#3554, PTAL and LMK if you have any questions, thanks!