### 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)

filelocs.append(dag.fileloc)
            if dag.is_subdag:
                orm_dag.is_subdag = True
                orm_dag.fileloc = dag.parent_dag.fileloc  
                orm_dag.root_dag_id = dag.parent_dag.dag_id  
                orm_dag.owners = dag.parent_dag.owner  
            else:
                orm_dag.is_subdag = False
                orm_dag.fileloc = dag.fileloc
                orm_dag.owners = dag.owner
            orm_dag.is_active = True
            orm_dag.has_import_errors = False
            orm_dag.last_parsed_time = timezone.utcnow()
            orm_dag.default_view = dag.default_view
            orm_dag.description = dag.description
            orm_dag.max_active_tasks = dag.max_active_tasks
            orm_dag.max_active_runs = dag.max_active_runs
            orm_dag.has_task_concurrency_limits = any(t.max_active_tis_per_dag is not None for t in dag.tasks)
            orm_dag.schedule_interval = dag.schedule_interval
            orm_dag.timetable_description = dag.timetable.description
            orm_dag.processor_subdir = processor_subdir
            run: DagRun | None = most_recent_runs.get(dag.dag_id)
            if run is None:
                data_interval = None
            else:
                data_interval = dag.get_run_data_interval(run)
            if num_active_runs.get(dag.dag_id, 0) >= orm_dag.max_active_runs:
                orm_dag.next_dagrun_create_after = None
            else:
                orm_dag.calculate_dagrun_date_fields(dag, data_interval)