## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin C', 'milligrams of vitamin A', 'grams of fat']. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic variables
The symbolic variables are $x_0$ for 'milligrams of vitamin C', $x_1$ for 'milligrams of vitamin A', and $x_2$ for 'grams of fat'.

## 3: Define the objective function in symbolic notation
The objective function to maximize is $3x_0 + 4x_1 + 9x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $6x_0 \leq 123$
- $7x_0 \leq 162$
- $8x_1 \leq 123$
- $15x_1 \leq 162$
- $14x_2 \leq 123$
- $8x_1 + 14x_2 \geq 39$
- $6x_0 + 14x_2 \geq 30$
- $8x_1 + 14x_2 \leq 88$
- $6x_0 + 8x_1 + 14x_2 \leq 88$
- $15x_1 + 16x_2 \leq 90$
- $7x_0 + 16x_2 \leq 108$
- $7x_0 + 15x_1 + 16x_2 \leq 108$

## 5: Create a symbolic representation of the problem
```json
{
'sym_variables': [
    ('x0', 'milligrams of vitamin C'), 
    ('x1', 'milligrams of vitamin A'), 
    ('x2', 'grams of fat')
],
'objective_function': '3*x0 + 4*x1 + 9*x2',
'constraints': [
    '6*x0 <= 123', 
    '7*x0 <= 162', 
    '8*x1 <= 123', 
    '15*x1 <= 162', 
    '14*x2 <= 123', 
    '8*x1 + 14*x2 >= 39', 
    '6*x0 + 14*x2 >= 30', 
    '8*x1 + 14*x2 <= 88', 
    '6*x0 + 8*x1 + 14*x2 <= 88', 
    '15*x1 + 16*x2 <= 90', 
    '7*x0 + 16*x2 <= 108', 
    '7*x0 + 15*x1 + 16*x2 <= 108'
]
}
```

## Step 6: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="milligrams_of_vitamin_C", lb=0)
    x1 = model.addVar(name="milligrams_of_vitamin_A", lb=0)
    x2 = model.addVar(name="grams_of_fat", lb=0)

    # Define the objective function
    model.setObjective(3*x0 + 4*x1 + 9*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(6*x0 <= 123)
    model.addConstr(7*x0 <= 162)
    model.addConstr(8*x1 <= 123)
    model.addConstr(15*x1 <= 162)
    model.addConstr(14*x2 <= 123)
    model.addConstr(8*x1 + 14*x2 >= 39)
    model.addConstr(6*x0 + 14*x2 >= 30)
    model.addConstr(8*x1 + 14*x2 <= 88)
    model.addConstr(6*x0 + 8*x1 + 14*x2 <= 88)
    model.addConstr(15*x1 + 16*x2 <= 90)
    model.addConstr(7*x0 + 16*x2 <= 108)
    model.addConstr(7*x0 + 15*x1 + 16*x2 <= 108)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin C: {x0.varValue}")
        print(f"Milligrams of vitamin A: {x1.varValue}")
        print(f"Grams of fat: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```