### Apache Airflow version

2.5.1

### What happened

DagRun state and start_date are reset if somebody is clearing a task of the running DagRun.

### What you think should happen instead

I think we should not reset DagRun `state` and `start_date` in it's in the running or queued states because it doesn't make any sense for me. `state` and `start_date` of the DgRun should remain the same in case somebody's clearing a task of the running DagRun

### How to reproduce

Let's say we have a Dag with 2 tasks in it - short one and the long one:
```
dag = DAG(
    'dummy-dag',
    schedule_interval='@once',
    catchup=False,
)

DagContext.push_context_managed_dag(dag)

bash_success = BashOperator(
    task_id='bash-success',
    bash_command='echo "Start and finish"; exit 0',
    retries=0,
)

date_ind_success = BashOperator(
    task_id='bash-long-success',
    bash_command='echo "Start and finish"; sleep 300; exit 0',
)
```

Let's day we have a running Dagrun of this DAG. First task finishes in a second and the long one is still running. We have a start_date and duration set and the Dagrun is still running. It runs for example for a 30 secs (pic 1 and 2)

<img width="486" alt="image" src="https://user-images.githubusercontent.com/23456894/225335210-c2223ad1-771b-459d-b8ed-8f0aacb9b890.png">
<img width="492" alt="image" src="https://user-images.githubusercontent.com/23456894/225335272-ad737aef-2051-4e27-ae36-38c76d720c95.png">

Then we are clearing the short task. It causes clear of the Dagrun state (to `queued`) and clears `start_date` like we have a new Dagrun (pic 3 and 4)
<img width="407" alt="image" src="https://user-images.githubusercontent.com/23456894/225335397-6c7e0df7-a26a-46ed-8eaa-56ff928fc01a.png">
<img width="498" alt="image" src="https://user-images.githubusercontent.com/23456894/225335491-4d6a860a-e923-4878-b212-a6ccb4b590a3.png">

### Operating System

Unix/MacOS

### Versions of Apache Airflow Providers

_No response_

### Deployment

Official Apache Airflow Helm Chart

### 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)

pool: str | None = None,
        cfg_path: str | None = None,
        map_index: int = -1,
    ) -> list[str]:
        """
        Generates the shell command required to execute this task instance.
        :param dag_id: DAG ID
        :param task_id: Task ID
        :param run_id: The run_id of this task's DagRun
        :param mark_success: Whether to mark the task as successful
        :param ignore_all_deps: Ignore all ignorable dependencies.
            Overrides the other ignore_* parameters.
        :param ignore_depends_on_past: Ignore depends_on_past parameter of DAGs
            (e.g. for Backfills)
        :param wait_for_past_depends_before_skipping: Wait for past depends before marking the ti as skipped
        :param ignore_task_deps: Ignore task-specific dependencies such as depends_on_past
            and trigger rule
        :param ignore_ti_state: Ignore the task instance's previous failure/success
        :param local: Whether to run the task locally
        :param pickle_id: If the DAG was serialized to the DB, the ID
            associated with the pickled DAG
        :param file_path: path to the file containing the DAG definition
        :param raw: raw mode (needs more details)
        :param job_id: job ID (needs more details)
        :param pool: the Airflow pool that the task should run in
        :param cfg_path: the Path to the configuration file
        :return: shell command that can be used to run the task instance
        """
        cmd = ["airflow", "tasks", "run", dag_id, task_id, run_id]
        if mark_success: