### 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)

return cls.bulk_write_to_db(dags=dags, session=session)
    @classmethod
    @provide_session
    def bulk_write_to_db(
        cls,
        dags: Collection[DAG],
        processor_subdir: str | None = None,
        session=NEW_SESSION,
    ):
        """
        Ensure the DagModel rows for the given dags are up-to-date in the dag table in the DB, including
        calculated fields.
        Note that this method can be called for both DAGs and SubDAGs. A SubDag is actually a SubDagOperator.
        :param dags: the DAG objects to save to the DB
        :return: None
        """
        if not dags:
            return
        log.info("Sync %s DAGs", len(dags))
        dag_by_ids = {dag.dag_id: dag for dag in dags}
        dag_ids = set(dag_by_ids.keys())
        query = (
            session.query(DagModel)
            .options(joinedload(DagModel.tags, innerjoin=False))
            .filter(DagModel.dag_id.in_(dag_ids))
            .options(joinedload(DagModel.schedule_dataset_references))
            .options(joinedload(DagModel.task_outlet_dataset_references))
        )
        orm_dags: list[DagModel] = with_row_locks(query, of=DagModel, session=session).all()
        existing_dags = {orm_dag.dag_id: orm_dag for orm_dag in orm_dags}