### Apache Airflow version

2.5.1

### What happened

Context: I am attempting to use [TypedDict](https://docs.python.org/3/library/typing.html#typing.TypedDict) objects to maintain the keys used in DAG params in a single place, and check for key names across multiple DAGs that use the params.
This raises an error with `mypy` as `params` expects an `Optional[Dict]`. Due to the invariance of `Dict`, this does not accept `TypedDict` objects.

What happened: I passed a `TypedDict` to the `params` arg of `DAG` and got a TypeError.

### What you think should happen instead

`TypedDict` objects should be accepted by `DAG`, which should accept `Optional[Mapping[str, Any]]`. 

Unless I'm mistaken, `params` are converted to a `ParamsDict` class and therefore the appropriate type hint is a generic `Mapping` type.

### How to reproduce

Steps to reproduce

```Python
from typing import TypedDict

from airflow import DAG
from airflow.models import Param

class ParamsTypedDict(TypedDict):
    str_param: Param

params: ParamsTypedDict = {
    "str_param": Param("", type="str")
}

with DAG(
    dag_id="mypy-error-dag",
    # The line below raises a mypy error
    # Argument "params" to "DAG" has incompatible type "ParamsTypedDict"; expected "Optional[Dict[Any, Any]]"  [arg-type]
    params=params,  
) as dag:
    pass
```

### Operating System

Amazon Linux

### Versions of Apache Airflow Providers

_No response_

### Deployment

Docker-Compose

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""Get the internal :class:`.Param` object for this key"""
        return self.__dict[key]
    def items(self):
        return ItemsView(self.__dict)
    def values(self):
        return ValuesView(self.__dict)
    def update(self, *args, **kwargs) -> None:
        if len(args) == 1 and not kwargs and isinstance(args[0], ParamsDict):
            return super().update(args[0].__dict)
        super().update(*args, **kwargs)
    def dump(self) -> dict[str, Any]:
        """Dumps the ParamsDict object as a dictionary, while suppressing exceptions"""
        return {k: v.resolve(suppress_exception=True) for k, v in self.items()}
    def validate(self) -> dict[str, Any]:
        """Validates & returns all the Params object stored in the dictionary"""
        resolved_dict = {}
        try:
            for k, v in self.items():
                resolved_dict[k] = v.resolve(suppress_exception=self.suppress_exception)
        except ParamValidationError as ve:
            raise ParamValidationError(f"Invalid input for param {k}: {ve}") from None
        return resolved_dict
    def serialize(self) -> dict[str, Any]:
        return self.dump()
    @staticmethod
    def deserialize(data: dict, version: int) -> ParamsDict:
        if version > ParamsDict.__version__:
            raise TypeError("serialized version > class version")
        return ParamsDict(data)
class DagParam(ResolveMixin):