### 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)

return self._template.format(cls=self._class_name, start=self._start_field, end=self._end_field)
def _get_model_data_interval(
    instance: Any,
    start_field_name: str,
    end_field_name: str,
) -> DataInterval | None:
    start = timezone.coerce_datetime(getattr(instance, start_field_name))
    end = timezone.coerce_datetime(getattr(instance, end_field_name))
    if start is None:
        if end is not None:
            raise InconsistentDataInterval(instance, start_field_name, end_field_name)
        return None
    elif end is None:
        raise InconsistentDataInterval(instance, start_field_name, end_field_name)
    return DataInterval(start, end)
def create_timetable(interval: ScheduleIntervalArg, timezone: Timezone) -> Timetable:
    """Create a Timetable instance from a ``schedule_interval`` argument."""
    if interval is NOTSET:
        return DeltaDataIntervalTimetable(DEFAULT_SCHEDULE_INTERVAL)
    if interval is None:
        return NullTimetable()
    if interval == "@once":
        return OnceTimetable()
    if isinstance(interval, (timedelta, relativedelta)):
        return DeltaDataIntervalTimetable(interval)
    if isinstance(interval, str):
        return CronDataIntervalTimetable(interval, timezone)
    raise ValueError(f"{interval!r} is not a valid schedule_interval.")
def get_last_dagrun(dag_id, session, include_externally_triggered=False):