### 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)

:param conf: Dict containing configuration/parameters to pass to the DAG
        :param creating_job_id: id of the job creating this DagRun
        :param session: database session
        :param dag_hash: Hash of Serialized DAG
        :param data_interval: Data interval of the DagRun
        """
        logical_date = timezone.coerce_datetime(execution_date)
        if data_interval and not isinstance(data_interval, DataInterval):
            data_interval = DataInterval(*map(timezone.coerce_datetime, data_interval))
        if data_interval is None and logical_date is not None:
            warnings.warn(
                "Calling `DAG.create_dagrun()` without an explicit data interval is deprecated",
                RemovedInAirflow3Warning,
                stacklevel=3,
            )
            if run_type == DagRunType.MANUAL:
                data_interval = self.timetable.infer_manual_data_interval(run_after=logical_date)
            else:
                data_interval = self.infer_automated_data_interval(logical_date)
        if run_type is None or isinstance(run_type, DagRunType):
            pass
        elif isinstance(run_type, str):  
            run_type = DagRunType(run_type)
        else:
            raise ValueError(f"`run_type` should be a DagRunType, not {type(run_type)}")
        if run_id:  
            if not isinstance(run_id, str):
                raise ValueError(f"`run_id` should be a str, not {type(run_id)}")
            inferred_run_type = DagRunType.from_run_id(run_id)
            if run_type is None: