[Feature Request]: Add optional reshuffle step to ReadAllFromParquet

### What would you like to happen?

We run a python beam batch pipeline in cloud dataflow that reads a large number of files from a list of gcs file patterns.

Something like:
```
input_files = ['gs://bucket/my_files_1*.parquet', 'gs://bucket/my_files_2*.parquet']
beam.Create(input_files) | beam.io.ReadAllFromParquet(...)
```
Each entry path is expanded to several 1000 files resulting in a "high fan-out detected" warning in the cloud dataflow UI.

The warning suggests adding an additional step between the `ReadAllFiles` and `_ArrowTableToRowDictionaries` steps as a way to improve performance:

```
ReadAllFiles/ReadAllFiles/ExpandIntoRanges, ReadEvents/ReadAllFiles/ReadAllFiles/ExpandIntoRanges, ReadEvents/ParDo(_ArrowTableToRowDictionaries)
```

However, those steps are in the `ReadAllFromParquet` [library](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/parquetio.py#L267-L268):
```
def expand(self, pvalue):
return pvalue | Read(self._source) | ParDo(_ArrowTableToRowDictionaries())
```

Because `Read(self._source)` goes from a few input file patterns to 10s of 1000s of actual files, adding a `beam.Reshuffle()` step between `Read(self._source)` and `ParDo(_ArrowTableToRowDictionaries()` would improve performance, according to the [cloud dataflow docs](https://cloud.google.com/dataflow/docs/guides/using-dataflow-insights#high-fan-out).

Having a configurable re-shuffle step:

```
beam.io.ReadAllFromParquet(..., reshuffle_after_read_files=True)

def expand(self, pvalue):
files = pvalue | Read(self._source)
if self._reshuffle_after_read_files:
files = files | beam.Resuffle()

return files | ParDo(_ArrowTableToRowDictionaries())
```
should help increase the performance when reading from an input pattern that is expanded into a large number of actual files.

### Issue Priority

Priority: 3 (nice-to-have improvement)

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