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)

Comienza en pyiceberg.io.pyarrow siguiendo la iteración por lotes de FileScanTask de ArrowScan y la construcción del PyArrow ds.Scanner. Confirma que los lotes se producen sin materializar de forma anticipada el iterador y que batch_size llega al scanner; se considera terminado cuando los archivos grandes pueden procesarse sin requerir que la tarea completa esté en memoria.

Escrito por el modelo de indexación a partir del texto del issue.