[Refactor] Refactor the step to include reward and done in the 'next' tensordict

[vmoens]

Description

Moves "reward" and "done" to the "next" tensordict.

New features:

• EnvBase._step now must return a TensorDict with a "next" key. This simplifies the downstream updates of the input tensordict as the t and t+1 assignments are clearly stated.
• Deprecation of the Specs class
• default values for reward_spec
• New done_spec with default value
• New output_spec that contains the reward_spec, the done_spec and the observation_spec
• observation_spec does not need to be a CompositeSpec anymore but output_spec must. If observation_spec is a composite spec, then a nested tensordict is to be expected in "next". => I decided not to proceed with this since having observation_spec as a CompositeSpec allows us to keep track of the obs names (e.g. pixels or observation). Concretely, this means that the "next" entry cannot be sampled using output_spec.rand() but using TensorDict({"reward": env.reward_spec.rand(), "done": env.done_spec.rand(), **env.observation_spec.rand()})
• Deprecates key selection in parallel envs (handled by env specs, keys must now match completely).

Things to sort out:

• Should we move the default done and reward specs to gym_like? Somewhere else?
• Should all envs have a done and reward? Maybe these things belong to gym-like? We could have envs that do not have rewards or done states (eg pure simulation)

cc @matteobettini @XuehaiPan @btx0424 @albertbou92 @BY571 @shagunsodhani

[matteobettini]

@vmoens. One thing maybe to add is that we should check that the "_reset" flag, used for resetting environements allways follows the same spec as done. This will be fundamental for avoiding errors in vectorized envs