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

Let's denote:
- $x_1$ as the amount of Pizza mix to be used
- $x_2$ as the amount of Mac and cheese mix to be used

The objective is to minimize the cost, which is $3x_1 + 3.25x_2$.

## Step 2: Define the constraints based on the minimum requirements

The minimum requirements are:
- 30 units of mozzarella: $4x_1 + x_2 \geq 30$
- 25 units of cheddar: $2x_1 + 5x_2 \geq 25$
- 5 units of salt: $x_1 + 2x_2 \geq 5$

Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the amounts of mixes cannot be negative.

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'Pizza mix'), ('x2', 'Mac and cheese mix')],
    'objective_function': '3*x1 + 3.25*x2',
    'constraints': [
        '4*x1 + x2 >= 30',
        '2*x1 + 5*x2 >= 25',
        'x1 + 2*x2 >= 5',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="Pizza_mix", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="Mac_and_cheese_mix", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(3*x1 + 3.25*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(4*x1 + x2 >= 30, name="Mozzarella_Constraint")
    model.addConstr(2*x1 + 5*x2 >= 25, name="Cheddar_Constraint")
    model.addConstr(x1 + 2*x2 >= 5, name="Salt_Constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Pizza mix: {x1.varValue}")
        print(f"Mac and cheese mix: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_cheese_mix_problem()
```