[FLINK-34123][docs][type] Add doc for built-in serialization support …

…for Map, List, and Collection

docs/content/docs/dev/datastream/fault-tolerance/serialization/types_serialization.md

@@ -37,15 +37,16 @@ Flink places some restrictions on the type of elements that can be in a DataStre
 The reason for this is that the system analyzes the types to determine
 efficient execution strategies.
 
-There are seven different categories of data types:
+There are eight different categories of data types:
 
 1. **Java Tuples** and **Scala Case Classes**
 2. **Java POJOs**
 3. **Primitive Types**
-4. **Regular Classes**
-5. **Values**
-6. **Hadoop Writables**
-7. **Special Types**
+4. **Common Collection Types**
+5. **Regular Classes**
+6. **Values**
+7. **Hadoop Writables**
+8. **Special Types**
 
 #### Tuples and Case Classes
 
@@ -167,6 +168,20 @@ input.keyBy(_.word)
 
 Flink supports all Java and Scala primitive types such as `Integer`, `String`, and `Double`.
 
+#### Common Collection Types
+
+Flink comes with dedicated serialization support for common Java collection types, which is more efficient than going
+through a general purpose serialization framework. Currently, only `Map`, `List` and its super interface `Collection`
+are supported. To utilize it, you need to declare the collection type with:
+
+1. Concrete type arguments: e.g. `List<String>` but not `List`, `List<T>`, or `List<?>`, as Flink needs them to dispatch
+   serialization of the element types.
+2. Interface types: e.g. `List<String>` but not `LinkedList<String>`, as Flink does not preserve the underlying
+   implementation types across serialization.
+
+Other nonqualified collection types will be handled by Flink as general class types. If the implementation types are
+also required to be preserved, you also need to register it with a custom serializer.
+
 #### General Class Types
 
 Flink supports most Java and Scala classes (API and custom).