Removing done from the llapi doc (#3810)

docs/Python-API.md

  `env.step()`).
  - `reward` is a float vector of length batch size. Corresponds to the
  rewards collected by each agent since the last simulation step.
- - `done` is an array of booleans of length batch size. Is true if the
- associated Agent was terminated during the last simulation step.
  - `agent_id` is an int vector of length batch size containing unique
  identifier for the corresponding Agent. This is used to track Agents
  across simulation steps.
  (Each array has one less dimension than the arrays in `DecisionSteps`)
  - `reward` is a float. Corresponds to the rewards collected by the agent
  since the last simulation step.
- - `done` is a bool. Is true if the Agent was terminated during the last
- simulation step.
  - `agent_id` is an int and an unique identifier for the corresponding Agent.
  - `action_mask` is an optional list of one dimensional array of booleans.
  Only available in multi-discrete action space type.
  `env.step()`).
  - `reward` is a float vector of length batch size. Corresponds to the
  rewards collected by each agent since the last simulation step.
- - `done` is an array of booleans of length batch size. Is true if the
- associated Agent was terminated during the last simulation step.
  - `agent_id` is an int vector of length batch size containing unique
  identifier for the corresponding Agent. This is used to track Agents
  across simulation steps.
  (Each array has one less dimension than the arrays in `TerminalSteps`)
  - `reward` is a float. Corresponds to the rewards collected by the agent
  since the last simulation step.
- - `done` is a bool. Is true if the Agent was terminated during the last
- simulation step.
  - `agent_id` is an int and an unique identifier for the corresponding Agent.
  - `max_step` is a bool. Is true if the Agent reached its maximum number of
  steps during the last simulation step.