## Step 1: Define the symbolic representation of the problem
Let's denote the number of premium sandwiches as $x_1$ and the number of regular sandwiches as $x_2$. The objective is to maximize profit, which is $5x_1 + x_2$. The constraints are:
- Preparation time: $6x_1 + 4x_2 \leq 400$
- Meat slices: $5x_1 + x_2 \leq 100$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## 2: Convert the problem into a symbolic representation
The symbolic representation can be written as:
```json
{
'sym_variables': [('x1', 'premium sandwiches'), ('x2', 'regular sandwiches')],
'objective_function': '5*x1 + x2',
'constraints': [
    '6*x1 + 4*x2 <= 400',
    '5*x1 + x2 <= 100',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 3: Translate the symbolic representation into Gurobi code
Now, let's translate this into Gurobi code in Python:

```python
import gurobi

def solve_sandwich_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="premium_sandwiches", lb=0, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="regular_sandwiches", lb=0, vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(5 * x1 + x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(6 * x1 + 4 * x2 <= 400, name="preparation_time")
    model.addConstr(5 * x1 + x2 <= 100, name="meat_slices")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of premium sandwiches: {x1.varValue}")
        print(f"Number of regular sandwiches: {x2.varValue}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_sandwich_problem()
```