Serialization/Deserialization doesn't handle Generics and generates Non-deterministic workflow error

🐛 **Describe the bug**
When a Pydantic Generic object is returned in an activity function `azure.functions._durable_functions._serialize_custom_object` returns "__class__": "ClassName[GenericSpecificClass]". When `azure.functions._durable_functions._deserialize_custom_object` is called in the calling orchestrator, getattr(module, "ClassName[GenericSpecificClass]") fails: ClassName exists but not the specialized "ClassName[GenericSpecificClass]".

🤔 **Expected behavior**
Serialization and deserialization handles Generics.

**Current behaviour**
Deserialization fails with `AttributeError: module 'xxx.xxx' has not attribute 'ClassName[GenericSpecificClass]'`and produces a Non-Deterministic Workflow error

☕ **Steps to reproduce**
```python
from pydantic import BaseModel
from typing import Generic
import azure.functions as func
import azure.durable_functions as df

class A(BaseModel, Generic[T]):
a: T

def to_json():
return self.model_dump(mode="json")

@classmethod
def from_json(cls, data: dict):
return cls(**data)

myApp = df.DFApp(http_auth_level=func.AuthLevel.ANONYMOUS)

@myApp.orchestration_trigger(context_name="context")
def orchestrator_function(context: df.DurableOrchestrationContext):
x = yield context.call_activity("hello", None)

@myApp.activity_trigger(input_name="params")
def hello(params):
return A[str](a="xxx")

```

Start by tracing azure.functions._durable_functions._serialize_custom_object and _deserialize_custom_object using the Pydantic Generic example in the issue. Reproduce the activity-to-orchestrator flow and inspect how the generic class name is serialized and looked up. Done means the example deserializes successfully without AttributeError or a non-deterministic workflow error.

Written by the indexing model from the issue text.