### Apache Airflow version

Other Airflow 2 version (please specify below)

### What happened

When running a DAG that is structured like:
```
@dag{dag_id="my_dag", default_args={"retries":0"}}
def dag():
    op = MyOperator.partial(task_id="my_task", retries=3).expand(...)
```

The following test fails:
```
    def test_retries(self) -> None:
        dag_bag = DagBag(dag_folder=DAG_FOLDER, include_examples=False)
        dag = dag_bag.dags["my_dag"]
        for task in dag.tasks:
            if "my_task" in task.task_id:
                self.assertEqual(3, task.retries) # fails - this is 0
```

When printing out `task.partial_kwargs`, and looking at how the default args and partial args are merged, it seems like the default args are always taking precedence, even though in the `partial` global function, the `retries` do get set later on with the task-level parameter value. This doesn't seem to be respected though.

### What you think should happen instead

_No response_

### How to reproduce

If you run my above unit test for a test DAG, on version 2.4.3, it should show up as a test failure.

### Operating System

OS Ventura

### Versions of Apache Airflow Providers

_No response_

### Deployment

Google Cloud Composer

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] 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)

\ -> x3 -> x5 /
    .. code-block:: python
        x1 = x1()
        x2 = x2()
        x3 = x3()
        x4 = x4()
        x5 = x5()
        x6 = x6()
        x1.set_downstream(x2)
        x1.set_downstream(x3)
        x2.set_downstream(x4)
        x3.set_downstream(x5)
        x4.set_downstream(x6)
        x5.set_downstream(x6)
    Using TaskGroups:
    .. code-block:: python
        chain(t1, task_group1, task_group2, t2)
        t1.set_downstream(task_group1)
        task_group1.set_downstream(task_group2)
        task_group2.set_downstream(t2)
    It is also possible to mix between classic operator/sensor, EdgeModifiers, XComArg, and TaskGroups:
    .. code-block:: python
        chain(t1, [Label("branch one"), Label("branch two")], [x1(), x2()], task_group1, x3())
    is equivalent to::
          / "branch one" -> x1 \
        t1                      -> task_group1 -> x3
          \ "branch two" -> x2 /
    .. code-block:: python
        x1 = x1()
        x2 = x2()