### 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 (and lock) a list of Dag objects that are due to create a new DagRun.
        This will return a resultset of rows that is row-level-locked with a "SELECT ... FOR UPDATE" query,
        you should ensure that any scheduling decisions are made in a single transaction -- as soon as the
        transaction is committed it will be unlocked.
        """
        from airflow.models.dataset import DagScheduleDatasetReference, DatasetDagRunQueue as DDRQ
        
        dataset_triggered_dag_info = {
            x.dag_id: (x.first_queued_time, x.last_queued_time)
            for x in session.query(
                DagScheduleDatasetReference.dag_id,
                func.max(DDRQ.created_at).label("last_queued_time"),
                func.min(DDRQ.created_at).label("first_queued_time"),
            )
            .join(DagScheduleDatasetReference.queue_records, isouter=True)
            .group_by(DagScheduleDatasetReference.dag_id)
            .having(func.count() == func.sum(case((DDRQ.target_dag_id.is_not(None), 1), else_=0)))
            .all()
        }
        dataset_triggered_dag_ids = set(dataset_triggered_dag_info.keys())
        if dataset_triggered_dag_ids:
            exclusion_list = {
                x.dag_id
                for x in (
                    session.query(DagModel.dag_id)
                    .join(DagRun.dag_model)
                    .filter(DagRun.state.in_((DagRunState.QUEUED, DagRunState.RUNNING)))
                    .filter(DagModel.dag_id.in_(dataset_triggered_dag_ids))