Fix ArrowScan materializing entire FileScanTask into memory

[ShreyeshArangath]

WIP not ready for review:
Closes #3036

Rationale for this change

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:

The internal scan logic uses a list() constructor on the batch iterator, forcing the entire file into memory.
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)

Are these changes tested?

Yes, tested

Are there any user-facing changes?

Yes, new API in ArrowScan to_record_batch_stream