Currently (as implemented in #17414), timetables are serialised by their classes’ full import path. This works most of the time, but not in some cases, including:

* Nested in class or function
* Declared directly in a DAG file without a valid import name (e.g. `12345.py`)

It’s fundamentally impossible to fix some of the cases (e.g. function-local class declaration) due to how Python works, but by requiring the user to explicitly register the timetable class, we can at least expose that problem so users don’t attempt to do that.

However, since the timetable actually would work a lot of times without any additional mechanism, I’m also wondering if we should _require_ registration.

1. Always require registration. A DAG using an unregistered timetable class fails to serialise.
2. Only require registration when the timetable class has wonky import path. “Normal” classes work out of the box without registering, and user sees a serialisation error asking for registration otherwise.
3. Don’t require registration. If a class cannot be correctly serialised, tell the user we can’t do it and the timetable must be declared another way.
airflow/serialization/serialized_objects.py
"""Helper function of depth first search for serialization.
        The serialization protocol is:
        (1) keeping JSON supported types: primitives, dict, list;
        (2) encoding other types as ``{TYPE: 'foo', VAR: 'bar'}``, the deserialization
            step decode VAR according to TYPE;
        (3) Operator has a special field CLASS to record the original class
            name for displaying in UI.
        """
        if cls._is_primitive(var):
            
            if isinstance(var, enum.Enum):
                return var.value
            return var
        elif isinstance(var, dict):
            return cls._encode({str(k): cls._serialize(v) for k, v in var.items()}, type_=DAT.DICT)
        elif isinstance(var, list):
            return [cls._serialize(v) for v in var]
        elif HAS_KUBERNETES and isinstance(var, k8s.V1Pod):
            json_pod = PodGenerator.serialize_pod(var)
            return cls._encode(json_pod, type_=DAT.POD)
        elif isinstance(var, DAG):
            return SerializedDAG.serialize_dag(var)
        elif isinstance(var, BaseOperator):
            return SerializedBaseOperator.serialize_operator(var)
        elif isinstance(var, cls._datetime_types):
            return cls._encode(var.timestamp(), type_=DAT.DATETIME)
        elif isinstance(var, datetime.timedelta):
            return cls._encode(var.total_seconds(), type_=DAT.TIMEDELTA)
        elif isinstance(var, Timezone):