Pluggable Backend Interface with DataFusion for Bounded-Memory Compute

## Summary

PyIceberg uses PyArrow as its sole execution engine. PyArrow is a kernel library with no memory management, no spill-to-disk, and no join operators. Operations that process more data than available memory (CoW deletes, equality delete resolution, scan planning for heavily-deleted tables, sorted writes) crash with OOM errors.

This issue tracks introducing a pluggable backend interface (`ReadBackend`, `WriteBackend`, `ComputeBackend` protocols) and integrating Apache DataFusion as the first bounded-memory compute backend.

## Problem

| Operation | Current Status | OOM Pattern |
|-----------|---------------|-------------|
| Equality delete reads | Hard `ValueError` | Anti-join requires all delete keys in memory |
| CoW delete (large files) | OOMs | Materializes entire Parquet file into RAM |
| Scan planning (>100K deletes) | OOMs | All delete entries in Python dict |
| Sort-on-write | Not implemented | Full sort before write |
| Positional deletes (millions) | OOMs | Python set of positions |

Tables written by Flink (which uses equality deletes) are completely unreadable by PyIceberg today.

## Solution

1. **Pluggable interface**: `ReadBackend`, `WriteBackend`, `ComputeBackend` protocols that decouple PyIceberg from PyArrow
2. **DataFusion integration**: Bounded-memory sort, join, and filter with spill-to-disk via `datafusion-python`
3. **Migration**: All existing data operations route through the interface with zero API changes

## Deliverables

- [ ] Equality delete resolution (NEW): tables with equality deletes can now be read
- [ ] CoW delete/overwrite streaming (FIX): statistics short-circuit + two-pass streaming
- [ ] Positional delete resolution (IMPROVED): bounded-memory for large delete sets
- [ ] Sort-on-write (NEW): external merge sort when DataFusion installed
- [ ] Bounded-memory scan planning (NEW): for tables with >100K delete files

## Related Issues

- #1210 - Support reading equality delete files
- #3270 - Equality Delete support
- #3554 - Integrate DataFusion as execution engine

## Acceptance Criteria

- All existing tests pass without `datafusion` installed (no regression)
- Tables with equality deletes return correct results
- CoW delete on 2GB+ files completes without OOM (with DataFusion)
- Sort-on-write produces sorted files when table has sort order and DataFusion installed
- Property-based tests verify PyArrow and DataFusion backends produce identical output

Không có tệp triển khai hoặc bài kiểm thử nào được nêu tên. Hãy bắt đầu bằng cách đọc các issue liên quan #1210, #3270 và #3554, sau đó xác định phạm vi của các protocol ReadBackend, WriteBackend và ComputeBackend được đề xuất cùng với phần tích hợp DataFusion. Công việc được xem là hoàn tất khi các hành vi delete, streaming, sắp xếp và lập kế hoạch quét được liệt kê đã được triển khai, không có hồi quy khi không có DataFusion, và kết quả của PyArrow và DataFusion là tương đương.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.