### 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)

include_upstream=False,
                include_downstream=True,
            )
            result.update(
                p_dag._get_task_instances(
                    task_ids=task_ids,
                    start_date=start_date,
                    end_date=end_date,
                    run_id=None,
                    state=state,
                    include_subdags=include_subdags,
                    include_parentdag=False,
                    include_dependent_dags=include_dependent_dags,
                    as_pk_tuple=True,
                    exclude_task_ids=exclude_task_ids,
                    session=session,
                    dag_bag=dag_bag,
                    recursion_depth=recursion_depth,
                    max_recursion_depth=max_recursion_depth,
                    visited_external_tis=visited_external_tis,
                )
            )
        if include_dependent_dags:
            
            from airflow.sensors.external_task import ExternalTaskMarker
            query = tis
            if as_pk_tuple:
                condition = TI.filter_for_tis(TaskInstanceKey(*cols) for cols in tis.all())
                if condition is not None:
                    query = session.query(TI).filter(condition)