## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B1', 'milligrams of vitamin B4', 'grams of protein'] which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables and their corresponding natural language objects are:
- $x_1$ : milligrams of vitamin B1
- $x_2$ : milligrams of vitamin B4
- $x_3$ : grams of protein

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is $9.32x_1 + 7.5x_2 + 2.63x_3$.

## Step 4: List the constraints in symbolic notation
The constraints are:
- $16x_1 \leq 212$
- $4x_1 \leq 417$
- $17x_1 \leq 105$
- $11x_1 \leq 107$
- $6x_2 \leq 212$
- $25x_2 \leq 417$
- $26x_2 \leq 105$
- $4x_2 \leq 107$
- $23x_3 \leq 212$
- $6x_3 \leq 417$
- $5x_3 \leq 105$
- $1x_3 \leq 107$
- $16x_1 + 23x_3 \geq 25$
- $6x_2 + 23x_3 \geq 23$
- $16x_1 + 6x_2 \leq 143$
- $6x_2 + 23x_3 \leq 74$
- $16x_1 + 23x_3 \leq 77$
- $16x_1 + 6x_2 + 23x_3 \leq 77$
- $25x_2 + 6x_3 \leq 232$
- $4x_1 + 25x_2 \leq 171$
- $4x_1 + 25x_2 + 6x_3 \leq 171$
- $17x_1 + 5x_3 \leq 40$
- $17x_1 + 26x_2 + 5x_3 \leq 40$
- $11x_1 + 4x_2 \leq 58$
- $11x_1 + 1x_3 \leq 49$
- $11x_1 + 4x_2 + 1x_3 \leq 49$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B1'), ('x2', 'milligrams of vitamin B4'), ('x3', 'grams of protein')],
'objective_function': '9.32x1 + 7.5x2 + 2.63x3',
'constraints': [
    '16x1 <= 212',
    '4x1 <= 417',
    '17x1 <= 105',
    '11x1 <= 107',
    '6x2 <= 212',
    '25x2 <= 417',
    '26x2 <= 105',
    '4x2 <= 107',
    '23x3 <= 212',
    '6x3 <= 417',
    '5x3 <= 105',
    '1x3 <= 107',
    '16x1 + 23x3 >= 25',
    '6x2 + 23x3 >= 23',
    '16x1 + 6x2 <= 143',
    '6x2 + 23x3 <= 74',
    '16x1 + 23x3 <= 77',
    '16x1 + 6x2 + 23x3 <= 77',
    '25x2 + 6x3 <= 232',
    '4x1 + 25x2 <= 171',
    '4x1 + 25x2 + 6x3 <= 171',
    '17x1 + 5x3 <= 40',
    '17x1 + 26x2 + 5x3 <= 40',
    '11x1 + 4x2 <= 58',
    '11x1 + 1x3 <= 49',
    '11x1 + 4x2 + 1x3 <= 49'
]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B1
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B4
    x3 = model.addVar(name="x3", lb=0)  # grams of protein

    # Define the objective function
    model.setObjective(9.32 * x1 + 7.5 * x2 + 2.63 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(16 * x1 <= 212)
    model.addConstr(4 * x1 <= 417)
    model.addConstr(17 * x1 <= 105)
    model.addConstr(11 * x1 <= 107)
    model.addConstr(6 * x2 <= 212)
    model.addConstr(25 * x2 <= 417)
    model.addConstr(26 * x2 <= 105)
    model.addConstr(4 * x2 <= 107)
    model.addConstr(23 * x3 <= 212)
    model.addConstr(6 * x3 <= 417)
    model.addConstr(5 * x3 <= 105)
    model.addConstr(1 * x3 <= 107)
    model.addConstr(16 * x1 + 23 * x3 >= 25)
    model.addConstr(6 * x2 + 23 * x3 >= 23)
    model.addConstr(16 * x1 + 6 * x2 <= 143)
    model.addConstr(6 * x2 + 23 * x3 <= 74)
    model.addConstr(16 * x1 + 23 * x3 <= 77)
    model.addConstr(16 * x1 + 6 * x2 + 23 * x3 <= 77)
    model.addConstr(25 * x2 + 6 * x3 <= 232)
    model.addConstr(4 * x1 + 25 * x2 <= 171)
    model.addConstr(4 * x1 + 25 * x2 + 6 * x3 <= 171)
    model.addConstr(17 * x1 + 5 * x3 <= 40)
    model.addConstr(17 * x1 + 26 * x2 + 5 * x3 <= 40)
    model.addConstr(11 * x1 + 4 * x2 <= 58)
    model.addConstr(11 * x1 + 1 * x3 <= 49)
    model.addConstr(11 * x1 + 4 * x2 + 1 * x3 <= 49)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B1: {x1.varValue}")
        print(f"Milligrams of vitamin B4: {x2.varValue}")
        print(f"Grams of protein: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```