Add maintenance action to remove dangling delete files

### Feature Request / Improvement

Streaming upsert writers (e.g. AWS Firehose Iceberg delivery) write equality-delete files on every commit. Compaction applies them into rewritten data files but leaves the entries in the manifests, and `expire_snapshots` can't touch files the current snapshot still references — so they accumulate without bound. On one of our production tables we measured ~90K dangling delete entries growing ~4.6K/day, and every query planning over recent partitions has to read the ever-growing delete manifests.

Java Iceberg handles this (`rewrite_data_files` with `remove-dangling-deletes`, `RemoveDanglingDeletesSparkAction`), but PyIceberg's `MaintenanceTable` currently only has `expire_snapshots`, and engines like Athena expose no statement for it either — so users on Athena/Firehose stacks have no non-Spark way out.

**Proposal:** `table.maintenance.remove_dangling_deletes()` — a metadata-only commit that:

- classifies per `(partition_spec_id, partition)`: an equality delete at sequence *s* is dangling iff no live data file in that partition has sequence < *s* (position deletes: <= *s*); ambiguous cases (unpartitioned specs, unknown content) are kept
- carries data manifests through unchanged, drops fully-dangling delete manifests, rewrites mixed ones to their surviving entries, and commits as a `replace` snapshot against the current ref

One enabler is worth a small standalone fix first: `ManifestWriterV2` hardcodes `content=data`, so PyIceberg currently can't write delete-content manifests at all.

We have a working implementation built on PyIceberg 0.12 internals (`write_manifest_list`, a `ManifestWriterV2` subclass, `AddSnapshotUpdate`/`SetSnapshotRefUpdate` with `AssertRefSnapshotId`), validated against production Glue/Athena tables, with a test matrix for the classification rules. Happy to contribute it if there's interest.

MaintenanceTable と ManifestWriterV2 から始め、次に参照されている write_manifest_list と snapshot 更新の内部実装を調査します。指定されたパーティションごとのシーケンス規則と分類テストマトリクスを受け入れ基準として使用します。完了とは、メタデータのみを置き換えるコミットが孤立した削除だけを削除し、曖昧なエントリとライブエントリを保持することです。

索引モデルが issue の本文から書いたものです。