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)

从 pyiceberg.io.pyarrow 开始，跟踪 ArrowScan 的 FileScanTask 批次迭代以及 PyArrow ds.Scanner 的构造。确认批次是在不预先完整物化迭代器的情况下生成的，并且 batch_size 能传递到 scanner；当大文件无需将整个任务放入内存即可处理时，即表示完成。

由索引模型根据 Issue 内容生成。