### 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)

orientation: str = conf.get_mandatory_value("webserver", "dag_orientation"),
    catchup: bool = conf.getboolean("scheduler", "catchup_by_default"),
    on_success_callback: None | DagStateChangeCallback | list[DagStateChangeCallback] = None,
    on_failure_callback: None | DagStateChangeCallback | list[DagStateChangeCallback] = None,
    doc_md: str | None = None,
    params: dict | None = None,
    access_control: dict | None = None,
    is_paused_upon_creation: bool | None = None,
    jinja_environment_kwargs: dict | None = None,
    render_template_as_native_obj: bool = False,
    tags: list[str] | None = None,
    owner_links: dict[str, str] | None = None,
    auto_register: bool = True,
) -> Callable[[Callable], Callable[..., DAG]]:
    """
    Python dag decorator. Wraps a function into an Airflow DAG.
    Accepts kwargs for operator kwarg. Can be used to parameterize DAGs.
    :param dag_args: Arguments for DAG object
    :param dag_kwargs: Kwargs for DAG object.
    """
    def wrapper(f: Callable) -> Callable[..., DAG]:
        @functools.wraps(f)
        def factory(*args, **kwargs):
            
            
            
            f_sig = signature(f).bind(*args, **kwargs)
            
            f_sig.apply_defaults()