[Python] saving a dataframe to the same partitioned location silently doubles the data

When a user saves a dataframe:
```python

df1.to_parquet('/tmp/table', partition_cols=['col_a'], engine='pyarrow')
```
it will create sub-directories named "`a=val1`", "`a=val2`" in `/tmp/table`. Each of them will contain one (or more?) parquet files with random filenames.

If a user runs the same command again, the code will use the existing sub-directories, but with different (random) filenames. As a result, any data loaded from this folder will be wrong - each row will be present twice.

For example, when using
```python

df1.to_parquet('/tmp/table', partition_cols=['col_a'], engine='pyarrow') # second time

df2 = pd.read_parquet('/tmp/table', engine='pyarrow')
assert len(df1) == len(df2) # raise an error
```
This is a subtle change in the data that can pass unnoticed.

I would expect that the code will prevent the user from using an non-empty destination as partitioned target. an overwrite flag can also be useful.

**Reporter**: [Tsvika Shapira](https://issues.apache.org/jira/browse/ARROW-7706)
#### Related issues:
- [[C++][Python][R][Dataset] Control overwriting vs appending when writing to existing dataset](https://github.com/apache/arrow/issues/28157) (is related to)

_{**Note**: *This issue was originally created as [ARROW-7706](https://issues.apache.org/jira/browse/ARROW-7706). Please see the [migration documentation](https://github.com/apache/arrow/issues/14542) for further details.*}