### 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)

source_query=query,
        target_table_name=target_table_name,
        source_table_name=source_table.name,
    )
    
    session.commit()
    metadata = reflect_tables([target_table_name], session)
    target_table = metadata.tables[target_table_name]
    where_clause = and_(*(source_table.c[x] == target_table.c[x] for x in uniqueness))
    if dialect_name == "sqlite":
        subq = query.selectable.with_only_columns([text(f"{source_table}.ROWID")])
        delete = source_table.delete().where(column("ROWID").in_(subq))
    else:
        delete = source_table.delete(where_clause)
    session.execute(delete)
def check_bad_references(session: Session) -> Iterable[str]:
    """
    Go through each table and look for records that can't be mapped to a dag run.
    When we find such "dangling" rows we back them up in a special table and delete them
    from the main table.
    Starting in Airflow 2.2, we began a process of replacing `execution_date` with `run_id` in many tables.
    """
    from airflow.models.dagrun import DagRun
    from airflow.models.renderedtifields import RenderedTaskInstanceFields
    from airflow.models.taskfail import TaskFail
    from airflow.models.taskinstance import TaskInstance
    from airflow.models.taskreschedule import TaskReschedule
    from airflow.models.xcom import XCom
    @dataclass
    class BadReferenceConfig: