### Apache Airflow Provider(s)

databricks

### Versions of Apache Airflow Providers

apache-airflow-providers-databricks==4.0.0

### Apache Airflow version

2.4.3

### Operating System

MAC OS

### Deployment

Virtualenv installation

### Deployment details

The issue is consistent across multiple Airflow deployments (locally on Docker Compose, remotely on MWAA in AWS, locally using virualenv)

### What happened

Passing `base_parameters` key into `notebook_task` parameter for `DatabricksSubmitRunOperator` as output of a previous task (TaskFlow paradigm) does not work.

After inspection of `DatabricksSubmitRunOperator.init` it seems that the problem relies on the fact that it uses `utils.databricks.normalise_json_content` to validate input parameters and, given that the input parameter is of type `PlainXComArg`, it fails to parse.

The workaround I found is to call it using `partial` and `expand`, which is a bit hacky and much less legible

### What you think should happen instead

`DatabricksSubmitRunOperator` should accept `PlainXComArg` arguments on init and eventually validate on `execute`, prior to submitting job run.

### How to reproduce

This DAG fails to parse:

```python3
with DAG(
    "dag_erroring",
    start_date=days_ago(1),
    params={"param_1": "", "param_2": ""},
) as dag:

    @task
    def from_dag_params_to_notebook_params(**context):

        # Transform/Validate DAG input parameters to sth expected by Notebook
        notebook_param_1 = context["dag_run"].conf["param_1"] + "abcd"
        notebook_param_2 = context["dag_run"].conf["param_2"] + "efgh"

        return {"some_param": notebook_param_1, "some_other_param": notebook_param_2}

    DatabricksSubmitRunOperator(
        task_id="my_notebook_task",
        new_cluster={
            "cluster_name": "single-node-cluster",
            "spark_version": "7.6.x-scala2.12",
            "node_type_id": "i3.xlarge",
            "num_workers": 0,
            "spark_conf": {
                "spark.databricks.cluster.profile": "singleNode",
                "spark.master": "[*, 4]",
            },
            "custom_tags": {"ResourceClass": "SingleNode"},
        },
        notebook_task={
            "notebook_path": "some/path/to/a/notebook",
            "base_parameters": from_dag_params_to_notebook_params(),
        },
        libraries=[],
        databricks_retry_limit=3,
        timeout_seconds=86400,
        polling_period_seconds=20,
    )
```

This one does not:

```python3
with DAG(
    "dag_parsing_fine",
    start_date=days_ago(1),
    params={"param_1": "", "param_2": ""},
) as dag:

    @task
    def from_dag_params_to_notebook_params(**context):

        # Transform/Validate DAG input parameters to sth expected by Notebook
        notebook_param_1 = context["dag_run"].conf["param_1"] + "abcd"
        notebook_param_2 = context["dag_run"].conf["param_2"] + "efgh"

        return [{"notebook_path": "some/path/to/a/notebook", "base_parameters":{"some_param": notebook_param_1, "some_other_param": notebook_param_2}}]

    DatabricksSubmitRunOperator.partial(
        task_id="my_notebook_task",
        new_cluster={
            "cluster_name": "single-node-cluster",
            "spark_version": "7.6.x-scala2.12",
            "node_type_id": "i3.xlarge",
            "num_workers": 0,
            "spark_conf": {
                "spark.databricks.cluster.profile": "singleNode",
                "spark.master": "[*, 4]",
            },
            "custom_tags": {"ResourceClass": "SingleNode"},
        },
        libraries=[],
        databricks_retry_limit=3,
        timeout_seconds=86400,
        polling_period_seconds=20,
    ).expand(notebook_task=from_dag_params_to_notebook_params())

```

### 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)

"""
    Submits a Spark job run to Databricks using the
    `api/2.1/jobs/runs/submit
    <https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit>`_
    API endpoint.
    There are three ways to instantiate this operator.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:DatabricksSubmitRunOperator`
    :param tasks: Array of Objects(RunSubmitTaskSettings) <= 100 items.
        .. seealso::
            https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit
    :param json: A JSON object containing API parameters which will be passed
        directly to the ``api/2.1/jobs/runs/submit`` endpoint. The other named parameters
        (i.e. ``spark_jar_task``, ``notebook_task``..) to this operator will
        be merged with this json dictionary if they are provided.
        If there are conflicts during the merge, the named parameters will
        take precedence and override the top level json keys. (templated)
        .. seealso::
            For more information about templating see :ref:`concepts:jinja-templating`.
            https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit
    :param spark_jar_task: The main class and parameters for the JAR task. Note that
        the actual JAR is specified in the ``libraries``.