python - read multiple parquets that have different schema?

### Describe the usage question you have. Please include as many useful details as possible.

because of schema evolution some parquet files have more columns than others.
i try to read them all in one go (there are over 20000 small files under different partition folders) because i want to write out their data into a single big file

```
original_files = pq.ParquetDataset("bucket/folder", filesystem=s3_src, partitioning="hive")
```

i get error:
```
ValueError: Schema in partition[year=0, month=0, day=14, hour=9] bucket/folder/year=2023/month=02/day=22/hour=14/redact.snappy.parquet was different.
```

how can i achieve this in pyarrow?
pyspark has an automatic mergeSchema option - https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#schema-merging

### Component(s)

Parquet, Python

Start at the pyarrow ParquetDataset entry point described in the issue and reproduce the schema mismatch with partitioned files. Compare the requested behavior with Spark's mergeSchema documentation; done means a supported way to read the dataset with differing schemas and write one combined file, covering the reported partition layout.

Written by the indexing model from the issue text.