### Apache Airflow version

main (development)

### What happened

When dag processor starts with a sub directory to process then the import errors are recorded with that path. So when there is processor for airflow-dag-processor-0 folder in order to remove import errors it lists all files under airflow-dag-processor-0 folder and deletes those not present. This becomes an issue when there is airflow-dag-processor-1 that records import errors whose files won't be part of airflow-dag-processor-0 folder. 

### What you think should happen instead

The fix would be to have processor_subdir stored in ImportError table so that during querying we only look at import errors relevant to the dag processor and don't delete other items. A fix similar to https://github.com/apache/airflow/pull/33357 needs to be applied for import errors as well.

### How to reproduce

1. create a dag file with import error at `~/airflow/dags/airflow-dag-processor-0/sample_sleep.py` . Start a dag processor with -S to process "~/airflow/dags/airflow-dag-processor-0/" . Import error should be present.
2. create a dag file with import error at `~/airflow/dags/airflow-dag-processor-1/sample_sleep.py` . Start a dag processor with -S to process "~/airflow/dags/airflow-dag-processor-1/". Import error for airflow-dag-processor-0 is deleted.
3. 

```python
from datetime import datetime, timedelta

from airflow import DAG
from airflow.decorators import task

from datetime import timedelta, invalid


with DAG(
    dag_id="task_duration",
    start_date=datetime(2023, 1, 1),
    catchup=True,
    schedule_interval="@daily",
) as dag:

    @task
    def sleeper():
        pass

    sleeper()
```

### Operating System

Ubuntu

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### 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)

self,
        file_path: str,
        callback_requests: list[CallbackRequest],
        pickle_dags: bool = False,
        session: Session = NEW_SESSION,
    ) -> tuple[int, int]:
        """
        Process a Python file containing Airflow DAGs.
        This includes:
        1. Execute the file and look for DAG objects in the namespace.
        2. Execute any Callbacks if passed to this method.
        3. Serialize the DAGs and save it to DB (or update existing record in the DB).
        4. Pickle the DAG and save it to the DB (if necessary).
        5. Mark any DAGs which are no longer present as inactive
        6. Record any errors importing the file into ORM
        :param file_path: the path to the Python file that should be executed
        :param callback_requests: failure callback to execute
        :param pickle_dags: whether serialize the DAGs found in the file and
            save them to the db
        :param session: Sqlalchemy ORM Session
        :return: number of dags found, count of import errors
        """
        self.log.info("Processing file %s for tasks to queue", file_path)
        try:
            dagbag = DagFileProcessor._get_dagbag(file_path)
        except Exception: