Merged PR 278: Enable the red agent to vary its start node

## Summary
- Made the data manipulation red agent be able to choose between the two clients to start operating on
- changed the attacker name in the config to 'data_manipulation_attacker' to because it is no longer tied to any client
- Updated the documentation notebook accordingly.
- Fixed a bug where the database client made a new connection every time it sent a SQL query (it tries to reuse its most recent one instead)
- Fixed a bug where link loads were not being cleared between episodes (?)

**warning** - the green agents are not working properly after reset right now, but I'm gonna fix this in the next ticket where I refactor episode reset.

## Test process
- unit tests pass
- UC2 notebook passes with both clients. (currently this doesn't work after an episode reset, but the very next thing I'm gonna work on is refactoring the reset, so I don't want to waste time fixing this.)

## Checklist
- [x] PR is linked to a **work item**
- [x] **acceptance criteria** of linked ticket are met
- [x] performed **self-review** of the code
- [x] written **tests** for any new functionality added with this PR
- [ ] updated the **documentation** if this PR changes or adds functionality
- [ ] written/updated **design docs** if this PR implements new functionality
- [x] updated the **change log**
- [x] ran **pre-commit** checks for code style
- [n] attended to any **TO-DOs** left in the code

Related work items: #2232

src/primaite/game/game.py

@@ -1,6 +1,6 @@
 """PrimAITE game - Encapsulates the simulation and agents."""
 from ipaddress import IPv4Address
-from typing import Dict, List
+from typing import Dict, List, Tuple
 
 from pydantic import BaseModel, ConfigDict
 
@@ -152,8 +152,14 @@ class PrimaiteGame:
             agent.update_reward(state)
             agent.reward_function.total_reward += agent.reward_function.current_reward
 
-    def apply_agent_actions(self) -> None:
-        """Apply all actions to simulation as requests."""
+    def apply_agent_actions(self) -> Dict[str, Tuple[str, Dict]]:
+        """
+        Apply all actions to simulation as requests.
+
+        :return: A recap of each agent's actions, in CAOS format.
+        :rtype: Dict[str, Tuple[str, Dict]]
+
+        """
         agent_actions = {}
         for agent in self.agents:
             obs = agent.observation_manager.current_observation