### Overview
Some Executors, currently a subset of the local Executors, run in a single threaded fashion and have certain limitations and requirements, many of which are hardcoded. To add a new single threaded Executor would require changes to core Airflow code.
Note: This coupling often shows up with SQLite compatibility checks since it does not support multiple connections.
### Examples
- **2a**) SQLite check done in configuration.py: https://github.com/apache/airflow/blob/26f94c5370587f73ebd935cecf208c6a36bdf9b6/airflow/configuration.py#L412-L419
- **2b**) When running in standalone mode SQLite compatibility is checked: https://github.com/apache/airflow/blob/26f94c5370587f73ebd935cecf208c6a36bdf9b6/airflow/cli/commands/standalone_command.py#L160-L165
- **2c**) Sensors in `poke` mode can block execution of DAGs when running with single process Executors, currently hardcoded to DebugExecutor (although should also include SequentialExecutor): https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/sensors/base.py#L243
### Proposal
A static method or attribute on the Executor class which can be checked by core code.

There is a precedent already set with the `supports_ad_hoc_ti_run` attribute, see:
https://github.com/apache/airflow/blob/fb741fd87254e235f99d7d67e558dafad601f253/airflow/executors/kubernetes_executor.py#L435 https://github.com/apache/airflow/blob/26f94c5370587f73ebd935cecf208c6a36bdf9b6/airflow/www/views.py#L1735-L1737
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from __future__ import annotations
from typing import Sequence
from airflow.callbacks.base_callback_sink import BaseCallbackSink
from airflow.callbacks.callback_requests import CallbackRequest
from airflow.configuration import conf
from airflow.executors.base_executor import CommandType, EventBufferValueType, QueuedTaskInstanceType
from airflow.executors.celery_executor import CeleryExecutor
from airflow.executors.kubernetes_executor import KubernetesExecutor
from airflow.models.taskinstance import SimpleTaskInstance, TaskInstance, TaskInstanceKey
from airflow.utils.log.logging_mixin import LoggingMixin
class CeleryKubernetesExecutor(LoggingMixin):
    """
    CeleryKubernetesExecutor consists of CeleryExecutor and KubernetesExecutor.
    It chooses an executor to use based on the queue defined on the task.
    When the queue is the value of ``kubernetes_queue`` in section ``[celery_kubernetes_executor]``
    of the configuration (default value: `kubernetes`), KubernetesExecutor is selected to run the task,
    otherwise, CeleryExecutor is used.
    """
    supports_ad_hoc_ti_run: bool = True
    supports_pickling: bool = True
    supports_sentry: bool = False
    callback_sink: BaseCallbackSink | None = None
    KUBERNETES_QUEUE = conf.get("celery_kubernetes_executor", "kubernetes_queue")
    is_local: bool = False
    serve_logs: bool = False
    def __init__(self, celery_executor: CeleryExecutor, kubernetes_executor: KubernetesExecutor):