### 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)

"""DAG run parameter reference.
    This binds a simple Param object to a name within a DAG instance, so that it
    can be resolved during the runtime via the ``{{ context }}`` dictionary. The
    ideal use case of this class is to implicitly convert args passed to a
    method decorated by ``@dag``.
    It can be used to parameterize a DAG. You can overwrite its value by setting
    it on conf when you trigger your DagRun.
    This can also be used in templates by accessing ``{{ context.params }}``.
    **Example**:
        with DAG(...) as dag:
          EmailOperator(subject=dag.param('subject', 'Hi from Airflow!'))
    :param current_dag: Dag being used for parameter.
    :param name: key value which is used to set the parameter
    :param default: Default value used if no parameter was set.
    """
    def __init__(self, current_dag: DAG, name: str, default: Any = NOTSET):
        if default is not NOTSET:
            current_dag.params[name] = default
        self._name = name
        self._default = default
    def iter_references(self) -> Iterable[tuple[Operator, str]]: