### 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)

raise TypeError(f"Chain not supported between instances of {type(up_task)} and {type(down_task)}")
        up_task_list = up_task
        down_task_list = down_task
        if len(up_task_list) != len(down_task_list):
            raise AirflowException(
                f"Chain not supported for different length Iterable. "
                f"Got {len(up_task_list)} and {len(down_task_list)}."
            )
        for up_t, down_t in zip(up_task_list, down_task_list):
            up_t.set_downstream(down_t)
def cross_downstream(
    from_tasks: Sequence[DependencyMixin],
    to_tasks: DependencyMixin | Sequence[DependencyMixin],
):
    r"""
    Set downstream dependencies for all tasks in from_tasks to all tasks in to_tasks.
    Using classic operators/sensors:
    .. code-block:: python
        cross_downstream(from_tasks=[t1, t2, t3], to_tasks=[t4, t5, t6])
    is equivalent to::
        t1 ---> t4
           \ /
        t2 -X -> t5
           / \
        t3 ---> t6
    .. code-block:: python
        t1.set_downstream(t4)
        t1.set_downstream(t5)
        t1.set_downstream(t6)
        t2.set_downstream(t4)