text: Title: [BUG] Several issues with NLinear with `normalize=True`
  Body: **Describe the bug**
  
  After reading the paper and having a closer look at the implementation of `DLinearModel`, I see several issues:
  
  1. The `seq_last` is only detached, but needs to be cloned as well. Otherwise, it it's values will be changed by line https://github.com/unit8co/darts/blob/8bea0713494e6d23321feaaec341fff4e6fb33dc/darts/models/forecasting/nlinear.py#L146 . This is because of the tensors still share the same underlying memory. The solution is: `seq_last = x[:, -1:, : self.output_dim].detach().clone()`
  
  2. Why is the normalization disabled by default? That's the whole catch of this model, so it should be enabled by default. (I could only imagine that it performed so bad due to the bug, that it was disabled by default)
  
  3. The normalization is only done for the target and not covariates (https://github.com/unit8co/darts/blob/8bea0713494e6d23321feaaec341fff4e6fb33dc/darts/models/forecasting/nlinear.py#L145). How I understand it, the original implementation from the paper does it also for the covariates (https://github.com/cure-lab/LTSF-Linear/blob/0c113668a3b88c4c4ee586b8c5ec3e539c4de5a6/models/NLinear.py#L27). And in my opinion, this makes totally sense.
  
  **To Reproduce**
  
  With normalize=True, NLinear currently fails to solve even very simple tasks:
  
  ```
  from darts import TimeSeries, models
  from darts.utils.timeseries_generation import linear_timeseries
  ts = linear_timeseries(start_value=0, end_value=10, length=10, freq='D')
  model = models.NLinearModel(
      input_chunk_length=3,
      output_chunk_length=1,
      n_epochs=10,
      batch_size=1,
      const_init=True,
      normalize=True # Explore the difference when durning on normalize
      )
  model.fit(ts)
  print(model.predict(1))
  ```
  
  **Expected behavior**
  
  Output around ~10.0
  
  **System (please complete the following information):**
   - Python version: 3.12
   - darts version 0.33.0
  
  **Additional context**
  Add any other context about the problem here.
  