### 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)

from airflow.datasets import Dataset
    from airflow.decorators import TaskDecoratorCollection
    from airflow.models.dagbag import DagBag
    from airflow.models.slamiss import SlaMiss
    from airflow.utils.task_group import TaskGroup
log = logging.getLogger(__name__)
DEFAULT_VIEW_PRESETS = ["grid", "graph", "duration", "gantt", "landing_times"]
ORIENTATION_PRESETS = ["LR", "TB", "RL", "BT"]
TAG_MAX_LEN = 100
DagStateChangeCallback = Callable[[Context], None]
ScheduleInterval = Union[None, str, timedelta, relativedelta]
ScheduleIntervalArg = Union[ArgNotSet, ScheduleInterval]
ScheduleArg = Union[ArgNotSet, ScheduleInterval, Timetable, Collection["Dataset"]]
SLAMissCallback = Callable[["DAG", str, str, List["SlaMiss"], List[TaskInstance]], None]
DEFAULT_SCHEDULE_INTERVAL = timedelta(days=1)
class InconsistentDataInterval(AirflowException):
    """Exception raised when a model populates data interval fields incorrectly.
    The data interval fields should either both be None (for runs scheduled
    prior to AIP-39), or both be datetime (for runs scheduled after AIP-39 is
    implemented). This is raised if exactly one of the fields is None.
    """
    _template = (
        "Inconsistent {cls}: {start[0]}={start[1]!r}, {end[0]}={end[1]!r}, "
        "they must be either both None or both datetime"
    )
    def __init__(self, instance: Any, start_field_name: str, end_field_name: str) -> None:
        self._class_name = type(instance).__name__
        self._start_field = (start_field_name, getattr(instance, start_field_name))
        self._end_field = (end_field_name, getattr(instance, end_field_name))
    def __str__(self) -> str: