ArrowScan materializes entire FileScanTask into memory

### Feature Request / Improvement

ArrowScan in PyIceberg does not support true streaming, leading to OOM failures when processing large files (file size > container size). While the API returns an iterator, the implementation eagerly materializes all record batches for a FileScanTask before yielding the first row.

Two primary bottlenecks were identified in the `pyiceberg.io.pyarrow` implementation:
1. The internal scan logic uses a `list()` constructor on the batch iterator, forcing the entire file into memory.
2. The `batch_size` parameter is not forwarded to the underlying PyArrow ds.Scanner, preventing granular memory control. Though, it does fallback to the standard

This behavior makes it impossible to process files larger than the available memory in distributed environments (e.g., Ray workers)

Start in pyiceberg.io.pyarrow by tracing ArrowScan's FileScanTask batch iteration and the construction of the PyArrow ds.Scanner. Confirm that batches are yielded without eagerly materializing the iterator and that batch_size reaches the scanner; done means large files can be processed without requiring the full task in memory.

Written by the indexing model from the issue text.