## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the number of burritos
- $x_2$ represents the number of tacos

The objective function to maximize profit is: $3x_1 + 3.50x_2$

The constraints based on the available resources are:
- $4x_1 + 4.5x_2 \leq 500$ (beef constraint)
- $4x_1 + 3x_2 \leq 400$ (toppings constraint)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints)

## Step 2: Convert the problem into a Gurobi code-ready format

The symbolic representation is as follows:
```json
{
    'sym_variables': [('x1', 'burritos'), ('x2', 'tacos')],
    'objective_function': '3*x1 + 3.50*x2',
    'constraints': [
        '4*x1 + 4.5*x2 <= 500',
        '4*x1 + 3*x2 <= 400',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Write the Gurobi code in Python

Now, let's write the Gurobi code:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="burritos", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="tacos", lb=0, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(3 * x1 + 3.5 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(4 * x1 + 4.5 * x2 <= 500, name="beef_constraint")
    model.addConstr(4 * x1 + 3 * x2 <= 400, name="toppings_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
        print(f"Max profit: ${model.objVal:.2f}")
    else:
        print("The model is infeasible")

solve_taco_restaurant_problem()
```