Expose per-file write metadata from DataFrame.write_parquet()

Inizia verificando se apache/datafusion#23656 è stato integrato, quindi leggi il binding Python per DataFrame.write_parquet() e la issue e la pull request collegate del core Rust. La forma dell’API è ancora aperta: la issue suggerisce di restituire i metadati direttamente o tramite un WriteResult. Il lavoro è completo quando i binding espongono i percorsi per file, i conteggi delle righe e le dimensioni in byte; i metadati serializzati sono opzionali.

Is your feature request related to a problem or challenge?

DataFrame.write_parquet() currently returns None. After writing, there is no way to retrieve per-file metadata (row counts, byte sizes, column statistics) for the files that were produced. This forces consumers that need file-level statistics — such as Apache Iceberg, Delta Lake, and Apache Hudi — to either:

1. Re-read Parquet footers from object storage after writing (extra I/O round-trips)
2. Bypass DataFusion's write pipeline entirely and use PyArrow's ParquetWriter with metadata_collector

This is a blocker for building a complete DataFusion-based write backend for table formats that require per-file column statistics in their commit metadata (e.g., Iceberg's DataFile entries need column_sizes, null_counts, lower_bounds, upper_bounds, split_offsets).

Describe the solution you'd like

After apache/datafusion#23472 / apache/datafusion#23656 lands in the Rust core, ParquetSink will expose a file_metadata() method returning per-file path, row count, and byte size. The Python bindings should surface this:

# Option A: write_parquet returns metadata directly
metadata = df.write_parquet("/path/to/output/")
# metadata: list[dict] = [
#     {"path": "part-0.parquet", "row_count": 500, "byte_size": 4096},
#     {"path": "part-1.parquet", "row_count": 500, "byte_size": 3840},
# ]

# Option B: write_parquet returns a WriteResult object
result = df.write_parquet("/path/to/output/")
result.count        # 1000
result.file_metadata  # list of per-file metadata dicts

At minimum, each file metadata entry should include:

• path (str): Object-store path of the written file
• row_count (int): Number of rows in this file
• byte_size (int): Sum of compressed row group sizes

Optionally (for full table-format integration):

• metadata (bytes | None): Serialized Parquet FileMetaData (Thrift compact), enabling consumers to extract column statistics without re-reading the file

Describe alternatives you've considered

• Return just the count (status quo): Insufficient for table format integration.
• Expose via a separate accessor: e.g. ctx.last_write_metadata() — awkward API, not composable.
• Return raw bytes of the full Parquet footer: Maximally informative but heavier. A structured dict with optional raw bytes is more ergonomic.

Additional context

• Upstream dependency: apache/datafusion#23656 adds DataSink::file_metadata() to the Rust core. This issue tracks exposing it through the Python bindings.
• Motivation: PyIceberg is building a pluggable execution backend with DataFusion for bounded-memory operations. A DataFusion write backend would enable single-pass Copy-on-Write deletes (read → filter → write entirely in Rust with spill-to-disk), but requires per-file metadata to construct Iceberg DataFile commit entries.
• Related: #1624 (per-session object store config) is the other piece needed for a complete DataFusion write backend in PyIceberg.