To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of pizza mixes used.
- $x_2$ as the number of mac and cheese mixes used.

The objective function aims to minimize the total cost, which can be represented algebraically as:
\[3x_1 + 3.25x_2\]

Given the minimum requirements for mozzarella, cheddar, and salt in the final product, we have the following constraints:

1. Mozzarella requirement: $4x_1 + x_2 \geq 30$
2. Cheddar requirement: $2x_1 + 5x_2 \geq 25$
3. Salt requirement: $x_1 + 2x_2 \geq 5$

Additionally, since we cannot use a negative number of mixes, we have non-negativity constraints:
\[x_1 \geq 0\]
\[x_2 \geq 0\]

Thus, the symbolic representation of the problem in JSON format is:

```json
{
    'sym_variables': [('x1', 'number of pizza mixes'), ('x2', 'number of mac and cheese mixes')],
    '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']
}
```

Now, let's write the Gurobi code in Python to solve this optimization problem:

```python
from gurobipy import *

# Create a new model
m = Model("Cheese_Mix_Optimization")

# Define variables
x1 = m.addVar(lb=0, name="pizza_mixes")
x2 = m.addVar(lb=0, name="mac_and_cheese_mixes")

# Set the objective function
m.setObjective(3*x1 + 3.25*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(4*x1 + x2 >= 30, "mozzarella_requirement")
m.addConstr(2*x1 + 5*x2 >= 25, "cheddar_requirement")
m.addConstr(x1 + 2*x2 >= 5, "salt_requirement")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of pizza mixes: {x1.x}")
    print(f"Number of mac and cheese mixes: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found")
```