[Python] pyarrow.concat_tables raises error about different Schema if columns have different order

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

why different columns order result in different schema?

the following code raise:

pyarrow.lib.ArrowInvalid: Schema at index 1 was different:

```python
import pandas as pd
import pyarrow as pa

print(pa.show_info())

df1 = pd.DataFrame({
"col1": [1,2,3,4,5],
"col2": ["A", "B", "C", "D", "E"],
})

df2 = pd.DataFrame({
"col2": ["A", "B", "C", "D", "E"],
"col1": [1,2,3,4,5],
})

tbl1 = pa.Table.from_pandas(df1, preserve_index=False)
tbl2 = pa.Table.from_pandas(df2, preserve_index=False)

tbl3 = pa.concat_tables([tbl1, tbl2])
```

### Component(s)

C++, Python

Run the pandas-to-PyArrow reproducer and compare tbl1.schema with tbl2.schema before concat_tables. Then review the concat_tables schema requirements in the Python and C++ APIs. Done should clarify the expected behavior for differing column order and document or identify the supported resolution.

Written by the indexing model from the issue text.