Upsert with 1M rows extremely slow due to `create_match_filter` and `txn.delete()` performance

### Apache Iceberg version

0.11.0

### Please describe the bug 🐞

CC @goutamvenkat-anyscale @koenvo @Fokko

Hello! We are implementing distributed writes from Ray Data to Iceberg. As part of upserts, we:

1. Write data files in parallel across Ray workers (each worker writes its share of Parquet files directly to storage and returns `DataFile` metadata + the upsert key columns back to the driver)
2. On the driver, concatenate all upsert keys collected from workers, call `create_match_filter` to build a delete predicate, then call `txn.delete()` followed by an append to commit

Upserting 1M rows (383 MiB) into an Iceberg table takes **~17.5 minutes**, almost entirely in the delete step:

```
create_match_filter (1M keys → In filter): 10.26s
txn.delete(): 1054.35s
append + commit: 1.14s
─────────────────────────────────────────────────────
Total upsert commit: 1065.75s
```

PyIceberg version `0.11.0`

This matches what's reported in #2159 and #2138.

The bottlenecks are:
1. **`create_match_filter`** — constructs a Python `BooleanExpression` node per row, which is expensive at 1M+ keys
2. **`txn.delete()`** — evaluates the resulting giant `In` expression against the table's data files with no partition pruning, effectively doing a full table scan

We have a few questions:

1. **Merge-on-read upserts** — is this on the roadmap, and if so, roughly when? MoR would let us avoid the expensive delete + rewrite cycle entirely for large upserts.
2. **Optimizing `create_match_filter` or `txn.delete()`** — is there a recommended way to speed these up today? For example, batching the `In` filter, or passing a partition-level hint to constrain the file scan?
3. **Partition-aware deletes** — if the upsert key columns overlap with partition columns, is there a supported way to restrict `txn.delete()` to only the relevant partitions, rather than scanning the full table?

## Related

- #2159 — Upserting large table extremely slow
- #2138 — Upsertion memory usage grows exponentially as table size grows
- #2943 — Optimize upsert performance for large datasets

### Willingness to contribute

- [ ] I can contribute a fix for this bug independently
- [x] I would be willing to contribute a fix for this bug with guidance from the Iceberg community
- [ ] I cannot contribute a fix for this bug at this time

從報告中描述的 create_match_filter 和 txn.delete() 進入點開始，然後根據提供的計時結果重現 1M 列 upsert 基準測試。查看相關 issue #2159、#2138 和 #2943，以了解現有內容。完成標準是取得經測量的改善，或提供一種有文件記錄且受支援的方法，以避免報告中的整個資料表刪除成本。

由索引模型根據 Issue 內容生成。