### 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)

stored_datasets = {}
        for dataset in all_datasets:
            stored_dataset = session.query(DatasetModel).filter(DatasetModel.uri == dataset.uri).first()
            if stored_dataset:
                
                
                
                stored_dataset.is_orphaned = expression.false()
                stored_datasets[stored_dataset.uri] = stored_dataset
            else:
                session.add(dataset)
                stored_datasets[dataset.uri] = dataset
        session.flush()  
        del all_datasets
        
        for dag_id, uri_list in dag_references.items():
            dag_refs_needed = {
                DagScheduleDatasetReference(dataset_id=stored_datasets[uri].id, dag_id=dag_id)
                for uri in uri_list
            }
            dag_refs_stored = set(
                existing_dags.get(dag_id)
                and existing_dags.get(dag_id).schedule_dataset_references  
                or []
            )
            dag_refs_to_add = {x for x in dag_refs_needed if x not in dag_refs_stored}
            session.bulk_save_objects(dag_refs_to_add)
            for obj in dag_refs_stored - dag_refs_needed:
                session.delete(obj)