### 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)

\ -> x3 -> x5 /
    .. code-block:: python
        x1 = x1()
        x2 = x2()
        x3 = x3()
        x4 = x4()
        x5 = x5()
        x6 = x6()
        x1.set_downstream(x2)
        x1.set_downstream(x3)
        x2.set_downstream(x4)
        x3.set_downstream(x5)
        x4.set_downstream(x6)
        x5.set_downstream(x6)
    Using TaskGroups:
    .. code-block:: python
        chain(t1, task_group1, task_group2, t2)
        t1.set_downstream(task_group1)
        task_group1.set_downstream(task_group2)
        task_group2.set_downstream(t2)
    It is also possible to mix between classic operator/sensor, EdgeModifiers, XComArg, and TaskGroups:
    .. code-block:: python
        chain(t1, [Label("branch one"), Label("branch two")], [x1(), x2()], task_group1, x3())
    is equivalent to::
          / "branch one" -> x1 \
        t1                      -> task_group1 -> x3
          \ "branch two" -> x2 /
    .. code-block:: python
        x1 = x1()
        x2 = x2()