[Python] PyArrow: write_dataset - Could not open CSV input source

Hi Arrow Community! 

Happy Friday! I am a new user to Arrow, specifically using pyarrow. However, I am very excited about the project. 

I am experiencing issues with the '{**}write_dataset'{**} function from the '{**}dataset{**}' module. Please forgive me, if this is a known issue. However, I have searched the GitHub 'Issues', as well as Stack Overflow and I have not identified a similar issue. 

I have a directory that contains 90 CSV files (essentially one CSV for each day between 2021-01-01 and 2021-03-31).  My objective was to read all the CSV files into a dataset and write the dataset to a single Parquet file format. Unfortunately, some of the CSV files contained nulls in some columns, which presented some issues which were resolved by specifying DataTypes with the following Stack Overflow solution:

[How do I specify a dtype for all columns when reading a CSV file with pyarrow?](https://stackoverflow.com/questions/71533197/how-do-i-specify-a-dtype-for-all-columns-when-reading-a-csv-file-with-pyarrow)

The following code works on the first pass.
```python

import pyarrow as pa
import pyarrow.csv as csv
import pyarrow.dataset as ds
import re
```
```python

pa.__version__
'8.0.0'
```
```python

column_types = {}
csv_path = '/home/user/csv_files'
field_re_pattern = "value_*"

# Open a dataset with the 'csv_path' path and 'csv' file format
# and assign to 'dataset1'
dataset1 = ds.dataset(csv_path, format='csv')

# Loop through each field in the 'dataset1' schema,
# match the 'field_re_pattern' regex pattern in the field name,
# and assign 'int64' DataType to the field.name in the 'column_types'
# dictionary
for field in (field for field in dataset1.schema \
              if re.match(field_re_pattern, field.name)):
        column_types[field.name] = pa.int64()

# Creates options for CSV data using the 'column_types' dictionary
# This returns a
convert_options = csv.ConvertOptions(column_types=column_types)

# Creates FileFormat for CSV using the 'convert_options'
# This returns a
custom_csv_format = ds.CsvFileFormat(convert_options=convert_options)

# Open a a dataset with the 'csv_path' path, instead of using the
# 'csv' file format, use the 'custom_csv_format' and assign to
# 'dataset2'
dataset2 = ds.dataset(csv_path, format=custom_csv_format)

# Write the 'dataset2' to the 'csv_path' base directory in the
# 'parquet' format, and overwrite/ignore if the file exists
ds.write_dataset(dataset2, base_dir=csv_path, format='parquet', existing_data_behavior='overwrite_or_ignore')
```
As previously stated, on first pass, the code works and creates a single parquet file (part-0.parquet) with the correct data, row count, and schema.

However, if the code is run again, the following error is encountered:
```python

ArrowInvalid: Could not open CSV input source '/home/user/csv_files/part-0.parquet': Invalid: CSV parse error: Row #2: Expected 4 columns, got 1: 6NQJRJV02XW$0Y8V p A$A18CEBS305DEM030TTW �5HZ50GCVJV1CSV
```
My interpretation of the error is that on the second pass the 'dataset2' variable now includes the 'part-0.parquet' file (which can be confirmed with the `dataset2.files` output showing the file) and the CSV reader is attempting to parse/read the parquet file.

If this is the case, is there an argument to ignore the parquet file and only evaluate the CSV files? Also, if a dataset object has a format of 'csv' or 'pyarrow._dataset.CsvFileFormat' associated with it would be nice to evaluate only CSV files and not all file types in the path. If that is not the current behavior.

If this is not the case, any ideas on the cause or solution?

Any assistance would be greatly appreciated.

Thank you and have a great day!

**Environment**: Pop!_OS 20.04 LTS OS & Conda 4.11.0 /Mamba 0.23.0 Environment
**Reporter**: [Earle Lyons](https://issues.apache.org/jira/browse/ARROW-16810)

_{**Note**: *This issue was originally created as [ARROW-16810](https://issues.apache.org/jira/browse/ARROW-16810). Please see the [migration documentation](https://github.com/apache/arrow/issues/14542) for further details.*}

Start with the Python reproduction using ds.dataset, CsvFileFormat, and ds.write_dataset, then inspect dataset2.files on the second pass. Confirm how the CSV dataset discovers files when part-0.parquet is present; done means the behavior is documented or corrected so the CSV reader does not attempt to parse the generated Parquet file.

Written by the indexing model from the issue text.