### 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)

missing_dag_ids = dag_ids.difference(existing_dags)
        for missing_dag_id in missing_dag_ids:
            orm_dag = DagModel(dag_id=missing_dag_id)
            dag = dag_by_ids[missing_dag_id]
            if dag.is_paused_upon_creation is not None:
                orm_dag.is_paused = dag.is_paused_upon_creation
            orm_dag.tags = []
            log.info("Creating ORM DAG for %s", dag.dag_id)
            session.add(orm_dag)
            orm_dags.append(orm_dag)
        
        most_recent_subq = (
            session.query(DagRun.dag_id, func.max(DagRun.execution_date).label("max_execution_date"))
            .filter(
                DagRun.dag_id.in_(existing_dags),
                or_(DagRun.run_type == DagRunType.BACKFILL_JOB, DagRun.run_type == DagRunType.SCHEDULED),
            )
            .group_by(DagRun.dag_id)
            .subquery()
        )
        most_recent_runs_iter = session.query(DagRun).filter(
            DagRun.dag_id == most_recent_subq.c.dag_id,
            DagRun.execution_date == most_recent_subq.c.max_execution_date,
        )
        most_recent_runs = {run.dag_id: run for run in most_recent_runs_iter}
        
        num_active_runs = DagRun.active_runs_of_dags(dag_ids=existing_dags, session=session)
        filelocs = []
        for orm_dag in sorted(orm_dags, key=lambda d: d.dag_id):
            dag = dag_by_ids[orm_dag.dag_id]