### 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)

start_date=execution_date,
            end_date=execution_date,
            dag_run_state=False,  
            session=session,
        )
        self.log.debug("Getting dagrun for dag %s", self.dag_id)
        dr: DagRun = _get_or_create_dagrun(
            dag=self,
            start_date=execution_date,
            execution_date=execution_date,
            run_id=DagRun.generate_run_id(DagRunType.MANUAL, execution_date),
            session=session,
            conf=run_conf,
        )
        tasks = self.task_dict
        self.log.debug("starting dagrun")
        
        
        
        while dr.state == State.RUNNING:
            schedulable_tis, _ = dr.update_state(session=session)
            for ti in schedulable_tis:
                add_logger_if_needed(ti)
                ti.task = tasks[ti.task_id]
                _run_task(ti, session=session)
        if conn_file_path or variable_file_path:
            
            secrets_backend_list.pop(0)
    @provide_session
    def create_dagrun(