feat: Introduce `FormattingConfig` and deprecate `DefaultFormats`

PR Checklist

[x] A description of the changes is added to the description of this PR.
[ ] If there is a related issue, make sure it is linked to this PR.
[x] If you've fixed a bug or added code that should be tested, add tests!
[x] If you've added or modified a feature, documentation in docs is updated

Description of changes

This PR proposes to deprecate the existing methods of configuring the output format of chispa through the use of the DefaultFormats and arbitrary dataclasses. Currently there are a few issues with this approach;

Poor error handling; what if a color does not exist?
How does a user know which colors are available?
Colors and formatting styles (e.g. bold) are all mixed together
A user can specify multiple colors without that raising an error,
Difficult to add proper static typing in the future
A user needs to provide all keys in his custom formats dataclass, even if they want to keep most the same to the defaults.
How do a user know which keys their custom dataclass should have?

As an alternative, this PR proposes to replace it with the classes Color, Style, Format, and FormattingConfig, so it can be used as

from chispa.formatting import FormattingConfig

formats = FormattingConfig(
        mismatched_rows={"color": "light_yellow"},
        matched_rows={"color": "cyan", "style": "bold"},
        mismatched_cells={"color": "purple"},
        matched_cells={"color": "blue"},
    )

assert_basic_rows_equality(df1.collect(), df2.collect(), formats=formats)

formats = FormattingConfig(
        mismatched_rows={"color": "light_yellow"},
    )

assert_basic_rows_equality(df1.collect(), df2.collect(), formats=formats)

or similarly:

from chispa.formatting import FormattingConfig, Color, Style

formats = FormattingConfig(
        mismatched_rows={"color": Color.LIGHT_YELLOW},
        matched_rows={"color": Color.CYAN, "style": Style.BOLD},
        mismatched_cells={"color": Color.PURPLE},
        matched_cells={"color": Color.BLUE},
    )

assert_basic_rows_equality(df1.collect(), df2.collect(), formats=formats)

This brings:

Improved code readability and maintainability.
Easier to extend and modify formatting options.
Enhanced error handling and validation of color and style inputs.

Notes

Even though we do not actively use type hints yet, this PR introduces them for the new classes. In the future we probably want to add type hints to the entire project and enable mypy. This PR also adds the line from __future__ import annotations to all files through the use of ruff, to provide type hinting compatiblity in Python 3.8 and 3.9
The changes should be backwards compatible; see test_deprectaed.py.

Running assert_basic_rows_equality(df1.collect(), df2.collect(), formats=CustomFormats()) will display

DeprecationWarning: Using an arbitrary dataclass for formatting is deprecated. Use `chispa.formatting.FormattingConfig` instead.

Running assert_basic_rows_equality(df1.collect(), df2.collect(), formats=DefaultFormats()) will display
```
DeprecationWarning: DefaultFormats is deprecated. Use `chispa.formatting.FormattingConfig` instead.
```

MrPowers / chispa

feat: Introduce `FormattingConfig` and deprecate `DefaultFormats` #127