Excessive synchronization in MemoryManager

Issue originally reported in Spark: https://issues.apache.org/jira/browse/SPARK-44003

We have a pyspark job that writes to a partitioned parquet dataset via:

```python

df.write.parquet(
path=path,
compression="snappy",
mode="overwrite",
partitionBy="year",
)
```

In this specific production case we partition by 28 distinct years, so 28 directories, each directory with 200 part files, total of 5.6K files. This particular job runs on a single dedicated and ephemeral VM. We have noticed that most of the time the VM is far from being saturated and the job is very slow. It's not IO or CPU bound. Here's an [annotated VM utilization graph ](https://gist.githubusercontent.com/ravwojdyla/e468bace2bc899f86348dee067173270/raw/03cfb383d49ad43adaec2eaa3d9cbf0a3c9b8c0b/VM_util.png). The blue line is CPU, and turquoise is memory. This graph doesn't show IO, but we have also monitored that, and it also was not saturated. On the labels:
- `BQ`, you can ignore this
- `SPARK~1` spark computes some data
- `SPARK~2` is 1st slow period
- `SPARK~3` is 2nd slow period

We took two 10 minute JFR profiles, those are marked `P-1` and `P-2` in the graph above. So `P-1` is solely in `SPARK~2`, and `P-2` is partially in `SPARK~2` but mostly in `SPARK~3`. Here's the [`P-1`](https://gist.githubusercontent.com/ravwojdyla/e468bace2bc899f86348dee067173270/raw/98c107ebd28608da55d84d13b3aa6eaf25b3c854/p1.png) profile, and here's [`P-2`](https://gist.githubusercontent.com/ravwojdyla/e468bace2bc899f86348dee067173270/raw/98c107ebd28608da55d84d13b3aa6eaf25b3c854/p2.png) profile.

The picture is a bit more clear when we look at the locks, here's the [report](https://gist.githubusercontent.com/ravwojdyla/e468bace2bc899f86348dee067173270/raw/c0f1fb78ac9d5f90a3106b4b43a3a7b27700f66a/locks.png). We see that the threads were blocked on locks for a total of 20.5h, mostly/specifically on the global `org.apache.parquet.hadoop.MemoryManager`, which has two synchronized methods: `addWriter` and `removeWriter`. From [parquet-mr GH src](https://github.com/apache/parquet-mr/blob/9d80330ae4948787ac0bf4e4b0d990917f106440/parquet-hadoop/src/main/java/org/apache/parquet/hadoop/MemoryManager.java#L77-L98):

{code:java}
/\*\*
- Add a new writer and its memory allocation to the memory manager.
- @param writer the new created writer
- @param allocation the requested buffer size
\*/
synchronized void addWriter(InternalParquetRecordWriter writer, Long allocation) {
Long oldValue = writerList.get(writer);
if (oldValue == null) {
writerList.put(writer, allocation);
} else {
throw new IllegalArgumentException("[BUG] The Parquet Memory Manager should not add an " +
"instance of InternalParquetRecordWriter more than once. The Manager already contains " +
"the writer: " + writer);
}
updateAllocation();
}

/\*\*
- Remove the given writer from the memory manager.
- @param writer the writer that has been closed
\*/
synchronized void removeWriter(InternalParquetRecordWriter writer) {
writerList.remove(writer);
if (!writerList.isEmpty()) {
updateAllocation();
}
}
{code}

During the 10 minute profiling session all worker threads were mostly waiting on this lock.

It appears that a combination of large number of writers created via Spark's `DynamicPartitionDataSingleWriter` and the `MemoryManager` synchronization bottleneck drastically reduces the performance by starving the writer threads.

**Reporter**: [Rafal Wojdyla](https://issues.apache.org/jira/secure/ViewProfile.jspa?name=ravwojdyla)
#### Related issues:
- [DynamicPartitionDataSingleWriter is being starved by Parquet MemoryManager](https://issues.apache.org/jira/browse/SPARK-44003) (relates to)
#### PRs and other links:
- [GitHub Pull Request #1240](https://github.com/apache/parquet-mr/pull/1240)

_{**Note**: *This issue was originally created as [PARQUET-2412](https://issues.apache.org/jira/browse/PARQUET-2412). Please see the [migration documentation](https://issues.apache.org/jira/browse/PARQUET-2502) for further details.*}

从 parquet-hadoop/src/main/java/org/apache/parquet/hadoop/MemoryManager.java 开始，重点查看同步的 addWriter 和 removeWriter 方法，并检查 JFR 和锁报告。检查相关的 DynamicPartitionDataSingleWriter 行为以及 PR #1240；完成的标准是解决同步瓶颈，同时保留 writer 注册和 allocation 更新。

由索引模型根据 Issue 内容生成。