### 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.models.taskinstance import TaskInstance, clear_task_instances
from airflow.models.taskmixin import DAGNode, DependencyMixin
from airflow.models.xcom import XCOM_RETURN_KEY
from airflow.serialization.enums import DagAttributeTypes
from airflow.ti_deps.deps.base_ti_dep import BaseTIDep
from airflow.ti_deps.deps.not_in_retry_period_dep import NotInRetryPeriodDep
from airflow.ti_deps.deps.not_previously_skipped_dep import NotPreviouslySkippedDep
from airflow.ti_deps.deps.prev_dagrun_dep import PrevDagrunDep
from airflow.ti_deps.deps.trigger_rule_dep import TriggerRuleDep
from airflow.triggers.base import BaseTrigger
from airflow.utils import timezone
from airflow.utils.context import Context
from airflow.utils.decorators import fixup_decorator_warning_stack
from airflow.utils.helpers import validate_key
from airflow.utils.operator_resources import Resources
from airflow.utils.session import NEW_SESSION, provide_session
from airflow.utils.trigger_rule import TriggerRule
from airflow.utils.weight_rule import WeightRule
if TYPE_CHECKING:
    import jinja2  
    from airflow.models.dag import DAG
    from airflow.models.taskinstance import TaskInstanceKey
    from airflow.models.xcom_arg import XComArg
    from airflow.utils.task_group import TaskGroup
ScheduleInterval = Union[str, timedelta, relativedelta]
TaskPreExecuteHook = Callable[[Context], None]
TaskPostExecuteHook = Callable[[Context, Any], None]
T = TypeVar("T", bound=FunctionType)
logger = logging.getLogger("airflow.models.baseoperator.BaseOperator")
def parse_retries(retries: Any) -> int | None: