chore: format get_customizable_model_schema return value (#9335)

api/core/model_runtime/docs/en_US/customizable_model_scale_out.md

@@ -218,7 +218,7 @@ For instance, Xinference supports `max_tokens`, `temperature`, and `top_p` param
 However, some vendors may support different parameters for different models. For example, the `OpenLLM` vendor supports `top_k`, but not all models provided by this vendor support `top_k`. Let's say model A supports `top_k` but model B does not. In such cases, we need to dynamically generate the model parameter schema, as illustrated below:
 
 ```python
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
         """
             used to define customizable model schema
         """

api/core/model_runtime/docs/zh_Hans/customizable_model_scale_out.md

@@ -205,7 +205,7 @@ provider_credential_schema:
   但是有的供应商根据不同的模型支持不同的参数，如供应商`OpenLLM`支持`top_k`，但是并不是这个供应商提供的所有模型都支持`top_k`，我们这里举例A模型支持`top_k`，B模型不支持`top_k`，那么我们需要在这里动态生成模型参数的Schema，如下所示：
   
     ```python
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
         """
             used to define customizable model schema
         """