### 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)

"""Metaclass of BaseOperator."""
    @classmethod
    def _apply_defaults(cls, func: T) -> T:
        """
        Function decorator that Looks for an argument named "default_args", and
        fills the unspecified arguments from it.
        Since python2.* isn't clear about which arguments are missing when
        calling a function, and that this can be quite confusing with multi-level
        inheritance and argument defaults, this decorator also alerts with
        specific information about the missing arguments.
        """
        
        
        
        
        sig_cache = signature(func)
        non_variadic_params = {
            name: param
            for (name, param) in sig_cache.parameters.items()
            if param.name != "self" and param.kind not in (param.VAR_POSITIONAL, param.VAR_KEYWORD)
        }
        non_optional_args = {
            name
            for name, param in non_variadic_params.items()
            if param.default == param.empty and name != "task_id"
        }
        fixup_decorator_warning_stack(func)
        @functools.wraps(func)
        def apply_defaults(self: BaseOperator, *args: Any, **kwargs: Any) -> Any: