### 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)

:param include_upstream: Include all upstream tasks of matched tasks,
            in addition to matched tasks.
        :param include_direct_upstream: Include all tasks directly upstream of matched
            and downstream (if include_downstream = True) tasks
        """
        from airflow.models.baseoperator import BaseOperator
        from airflow.models.mappedoperator import MappedOperator
        
        
        memo = {id(self.task_dict): None, id(self._task_group): None}
        dag = copy.deepcopy(self, memo)  
        if isinstance(task_ids_or_regex, (str, re.Pattern)):
            matched_tasks = [t for t in self.tasks if re.findall(task_ids_or_regex, t.task_id)]
        else:
            matched_tasks = [t for t in self.tasks if t.task_id in task_ids_or_regex]
        also_include: list[Operator] = []
        for t in matched_tasks:
            if include_downstream:
                also_include.extend(t.get_flat_relatives(upstream=False))
            if include_upstream:
                also_include.extend(t.get_flat_relatives(upstream=True))
        direct_upstreams: list[Operator] = []
        if include_direct_upstream:
            for t in itertools.chain(matched_tasks, also_include):
                upstream = (u for u in t.upstream_list if isinstance(u, (BaseOperator, MappedOperator)))
                direct_upstreams.extend(upstream)
        
        
        
        def _deepcopy_task(t) -> Operator: