## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B6', 'milligrams of vitamin B2', and 'milligrams of vitamin B1', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $4x_0 + 4x_1 + 2x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $2x_0 \leq 115$
- $15x_0 \leq 90$
- $14x_0 \leq 81$
- $16x_0 \leq 85$
- $2x_1 \leq 115$
- $12x_1 \leq 90$
- $13x_1 \leq 81$
- $5x_1 \leq 85$
- $13x_2 \leq 115$
- $x_2 \leq 90$
- $11x_2 \leq 81$
- $6x_2 \leq 85$
- $15x_0 + 12x_1 \geq 23$
- $15x_0 + x_2 \geq 27$
- $14x_0 + 13x_1 \geq 12$
- $5x_1 + 6x_2 \geq 11$
- $16x_0 + 5x_1 + 6x_2 \geq 15$
- $2x_1 + 13x_2 \leq 96$
- $2x_0 + 2x_1 + 13x_2 \leq 96$
- $15x_0 + x_2 \leq 74$
- $12x_1 + x_2 \leq 58$
- $15x_0 + 12x_1 + x_2 \leq 58$
- $14x_0 + 13x_1 \leq 77$
- $14x_0 + 11x_2 \leq 75$
- $13x_1 + 11x_2 \leq 79$
- $14x_0 + 13x_1 + 11x_2 \leq 79$
- $5x_1 + 6x_2 \leq 35$
- $16x_0 + 5x_1 + 6x_2 \leq 35$

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B6'), 
        ('x1', 'milligrams of vitamin B2'), 
        ('x2', 'milligrams of vitamin B1')
    ], 
    'objective_function': '4*x0 + 4*x1 + 2*x2', 
    'constraints': [
        '2*x0 <= 115',
        '15*x0 <= 90',
        '14*x0 <= 81',
        '16*x0 <= 85',
        '2*x1 <= 115',
        '12*x1 <= 90',
        '13*x1 <= 81',
        '5*x1 <= 85',
        '13*x2 <= 115',
        'x2 <= 90',
        '11*x2 <= 81',
        '6*x2 <= 85',
        '15*x0 + 12*x1 >= 23',
        '15*x0 + x2 >= 27',
        '14*x0 + 13*x1 >= 12',
        '5*x1 + 6*x2 >= 11',
        '16*x0 + 5*x1 + 6*x2 >= 15',
        '2*x1 + 13*x2 <= 96',
        '2*x0 + 2*x1 + 13*x2 <= 96',
        '15*x0 + x2 <= 74',
        '12*x1 + x2 <= 58',
        '15*x0 + 12*x1 + x2 <= 58',
        '14*x0 + 13*x1 <= 77',
        '14*x0 + 11*x2 <= 75',
        '13*x1 + 11*x2 <= 79',
        '14*x0 + 13*x1 + 11*x2 <= 79',
        '5*x1 + 6*x2 <= 35',
        '16*x0 + 5*x1 + 6*x2 <= 35'
    ]
}
```

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

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x0")
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x2")

    # Objective function
    model.setObjective(4*x0 + 4*x1 + 2*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2*x0 <= 115)
    model.addConstr(15*x0 <= 90)
    model.addConstr(14*x0 <= 81)
    model.addConstr(16*x0 <= 85)
    model.addConstr(2*x1 <= 115)
    model.addConstr(12*x1 <= 90)
    model.addConstr(13*x1 <= 81)
    model.addConstr(5*x1 <= 85)
    model.addConstr(13*x2 <= 115)
    model.addConstr(x2 <= 90)
    model.addConstr(11*x2 <= 81)
    model.addConstr(6*x2 <= 85)
    model.addConstr(15*x0 + 12*x1 >= 23)
    model.addConstr(15*x0 + x2 >= 27)
    model.addConstr(14*x0 + 13*x1 >= 12)
    model.addConstr(5*x1 + 6*x2 >= 11)
    model.addConstr(16*x0 + 5*x1 + 6*x2 >= 15)
    model.addConstr(2*x1 + 13*x2 <= 96)
    model.addConstr(2*x0 + 2*x1 + 13*x2 <= 96)
    model.addConstr(15*x0 + x2 <= 74)
    model.addConstr(12*x1 + x2 <= 58)
    model.addConstr(15*x0 + 12*x1 + x2 <= 58)
    model.addConstr(14*x0 + 13*x1 <= 77)
    model.addConstr(14*x0 + 11*x2 <= 75)
    model.addConstr(13*x1 + 11*x2 <= 79)
    model.addConstr(14*x0 + 13*x1 + 11*x2 <= 79)
    model.addConstr(5*x1 + 6*x2 <= 35)
    model.addConstr(16*x0 + 5*x1 + 6*x2 <= 35)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```