[Python][Docs] Opening a partitioned dataset with schema and filter

Add a note to the docs that if partitioning and schema are both specified at opening of a dataset and partitioning names are not included in the data, schema needs to include the partitioning names (directory or hive partitioning) in a case that filtering will be done.

Example:

```python

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pyarrow.dataset as ds

# Define the data
table = pa.table({'one': [-1, np.nan, 2.5],
'two': ['foo', 'bar', 'baz'],
'three': [True, False, True]})

# Write to partitioned dataset
# The files will include columns "two" and "three"
pq.write_to_dataset(table, root_path='dataset_name',
partition_cols=['one'])

# Reading the partitioned dataset with schema not including partitioned names
# will error

schema = pa.schema([("three", "double")])
data = ds.dataset("dataset_name", partitioning="hive", schema=schema)
subset = ds.field("one") == 2.5
data.to_table(filter=subset)

# And will not if done like so:
schema = pa.schema([("three", "double"), ("one", "double")])
data = ds.dataset("dataset_name", partitioning="hive", schema=schema)
subset = ds.field("one") == 2.5
data.to_table(filter=subset)

```

**Reporter**: [Alenka Frim](https://issues.apache.org/jira/browse/ARROW-15311) / @AlenkaF

_{**Note**: *This issue was originally created as [ARROW-15311](https://issues.apache.org/jira/browse/ARROW-15311). Please see the [migration documentation](https://github.com/apache/arrow/issues/14542) for further details.*}

Start by locating the Python dataset-opening documentation covering partitioning, schemas, and filters. Add the provided partitioned-dataset example or a concise note explaining that the schema must include partitioning names when filters reference them, and verify that the documented behavior matches the example.

Written by the indexing model from the issue text.