refactor: used pydantic for object mapping

[lefcha]

This is a rebase of the original PR #129 by @BoggerByte

This pull request replaces hardcoded object mapping methods with Pydantic models, enhancing flexibility and readability.

Changes Made

• Consolidated object mapping methods from_dict and to_dict into a unified approach leveraging Pydantic's model capabilities.
• Add some missing type annotations.
• Addressed a bug related to models default values.

This PR resolves #128 and aligns the project with best practices for object mapping and data validation using Pydantic.
Your feedback and review are highly appreciated!

• pytest passed
• mypy no errors
• ruff no errors

[proxi]

The direction of change looks good, but we cannot just add such a major dependency without bumping the major version. I asked @goncalossilva and he said:

revamping our Python SDK for vNEXT is on my list for the quarter. This will be done under a new major version, of course. And this way we can adopt Pydantic.

So I think we need to put this on hold for now.

[goncalossilva]

Thank you for this, @lefcha & @BoggerByte!

Let's target a new branch, v3, and merge this there, please? @proxi do you have concers with that?

[BoggerByte]

I understand that choosing Pydantic as a dependency might have been a bit heavy in terms of size and actual usage scope. If all we need is to parse and serialize models, a better choice might be dataclasses-json. It’s essentially a lightweight alternative to Pydantic and, in my opinion, a perfect fit for this project 😃

Here's an example of dataclasses-json usage I found on their project main page:

@dataclass_json
@dataclass
class Person:
    name: str


person = Person(name='lidatong')
person.to_json()  # '{"name": "lidatong"}' <- this is a string
person.to_dict()  # {'name': 'lidatong'} <- this is a dict
Person.from_json('{"name": "lidatong"}')  # Person(1)
Person.from_dict({'name': 'lidatong'})  # Person(1)

[BoggerByte]

Switched to dataclasses_json for object mapping to simplify serialization and reduce overhead in new #163 PR. @proxi, @pkoch, @goncalossilva, let me know if you see any issues with this approach!