### 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)

schedule=schedule,
                owner_links=owner_links,
                auto_register=auto_register,
            ) as dag_obj:
                
                if f.__doc__ and not dag_obj.doc_md:
                    dag_obj.doc_md = f.__doc__
                
                
                f_kwargs = {}
                for name, value in f_sig.arguments.items():
                    f_kwargs[name] = dag_obj.param(name, value)
                
                back = sys._getframe().f_back
                dag_obj.fileloc = back.f_code.co_filename if back else ""
                
                f(**f_kwargs)
            
            return dag_obj
        
        fixup_decorator_warning_stack(factory)
        return factory
    return wrapper
STATICA_HACK = True
globals()["kcah_acitats"[::-1].upper()] = False
if STATICA_HACK:  
    from airflow.models.serialized_dag import SerializedDagModel
    DagModel.serialized_dag = relationship(SerializedDagModel)
    """:sphinx-autoapi-skip:"""
class DagContext: