text: Title: Bug: Using 2 LoRA configs with `target_modules='all-linear'` leads to nested LoRA layers
  Body: ### System Info
  
  -
  
  ### Who can help?
  
  _No response_
  
  ### Information
  
  - [ ] The official example scripts
  - [ ] My own modified scripts
  
  ### Tasks
  
  - [ ] An officially supported task in the `examples` folder
  - [ ] My own task or dataset (give details below)
  
  ### Reproduction
  
  ```python
  from transformers import AutoModelForCausalLM
  from peft import LoraConfig, get_peft_model
  
  model_id = "hf-internal-testing/tiny-random-OPTForCausalLM"
  model = AutoModelForCausalLM.from_pretrained(model_id)
  config0 = LoraConfig(target_modules="all-linear")
  config1 = LoraConfig(target_modules="all-linear")
  model = get_peft_model(model, config0)#, adapter_name="default")
  model.add_adapter("adapter1", config1)
  print(model.base_model.model.model.decoder.layers[0].self_attn.k_proj)
  ```
  
  prints:
  
  ```
  lora.Linear(
    (base_layer): lora.Linear(
      (base_layer): Linear(in_features=16, out_features=16, bias=True)
      (lora_dropout): ModuleDict(
        (adapter1): Identity()
      )
      (lora_A): ModuleDict(
        (adapter1): Linear(in_features=16, out_features=8, bias=False)
      )
      (lora_B): ModuleDict(
        (adapter1): Linear(in_features=8, out_features=16, bias=False)
      )
      (lora_embedding_A): ParameterDict()
      (lora_embedding_B): ParameterDict()
      (lora_magnitude_vector): ModuleDict()
    )
    (lora_dropout): ModuleDict(
      (default): Identity()
    )
    (lora_A): ModuleDict(
      (default): lora.Linear(
        (base_layer): Linear(in_features=16, out_features=8, bias=False)
        (lora_dropout): ModuleDict(
          (adapter1): Identity()
        )
        (lora_A): ModuleDict(
          (adapter1): Linear(in_features=16, out_features=8, bias=False)
        )
        (lora_B): ModuleDict(
          (adapter1): Linear(in_features=8, out_features=8, bias=False)
        )
        (lora_embedding_A): ParameterDict()
        (lora_embedding_B): ParameterDict()
        (lora_magnitude_vector): ModuleDict()
      )
    )
    (lora_B): ModuleDict(
      (default): lora.Linear(
        (base_layer): Linear(in_features=8, out_features=16, bias=False)
        (lora_dropout): ModuleDict(
          (adapter1): Identity()
        )
        (lora_A): ModuleDict(
          (adapter1): Linear(in_features=8, out_features=8, bias=False)
        )
        (lora_B): ModuleDict(
          (adapter1): Linear(in_features=8, out_features=16, bias=False)
        )
        (lora_embedding_A): ParameterDict()
        (lora_embedding_B): ParameterDict()
        (lora_magnitude_vector): ModuleDict()
      )
    )
    (lora_embedding_A): ParameterDict()
    (lora_embedding_B): ParameterDict()
    (lora_magnitude_vector): ModuleDict()
  )
  ```
  
  ### Expected behavior
  
  Instead of getting nested LoRA layers, the linear layers belonging to a LoRA layer should not be targeted by `all-linear`.