### 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)

partial_kwargs.setdefault("resources", resources)
    partial_kwargs.setdefault("doc", doc)
    partial_kwargs.setdefault("doc_json", doc_json)
    partial_kwargs.setdefault("doc_md", doc_md)
    partial_kwargs.setdefault("doc_rst", doc_rst)
    partial_kwargs.setdefault("doc_yaml", doc_yaml)
    
    if "task_concurrency" in kwargs:  
        raise TypeError("unexpected argument: task_concurrency")
    if partial_kwargs["wait_for_downstream"]:
        partial_kwargs["depends_on_past"] = True
    partial_kwargs["start_date"] = timezone.convert_to_utc(partial_kwargs["start_date"])
    partial_kwargs["end_date"] = timezone.convert_to_utc(partial_kwargs["end_date"])
    if partial_kwargs["pool"] is None:
        partial_kwargs["pool"] = Pool.DEFAULT_POOL_NAME
    partial_kwargs["retries"] = parse_retries(partial_kwargs["retries"])
    partial_kwargs["retry_delay"] = coerce_timedelta(partial_kwargs["retry_delay"], key="retry_delay")
    if partial_kwargs["max_retry_delay"] is not None:
        partial_kwargs["max_retry_delay"] = coerce_timedelta(
            partial_kwargs["max_retry_delay"],
            key="max_retry_delay",
        )
    partial_kwargs["executor_config"] = partial_kwargs["executor_config"] or {}
    partial_kwargs["resources"] = coerce_resources(partial_kwargs["resources"])
    return OperatorPartial(
        operator_class=operator_class,
        kwargs=partial_kwargs,
        params=partial_params,
    )
class BaseOperatorMeta(abc.ABCMeta):