## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin E', 'grams of protein', and 'milligrams of zinc', 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 symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin E
- $x_1$ : grams of protein
- $x_2$ : milligrams of zinc

## 3: Define the objective function in symbolic notation
The objective function to minimize is $7.61x_0 + 3.07x_1 + 6.17x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3x_0 \leq 91$
- $5x_0 \leq 66$
- $3x_0 \leq 102$
- $14x_1 \leq 91$
- $12x_1 \leq 66$
- $7x_1 \leq 102$
- $14x_2 \leq 91$
- $2x_2 \leq 66$
- $3x_2 \leq 102$
- $3x_0 + 14x_2 \geq 24$
- $14x_1 + 14x_2 \geq 30$
- $3x_0 + 14x_1 \geq 17$
- $3x_0 + 14x_1 + 14x_2 \geq 17$
- $5x_0 + 2x_2 \geq 8$
- $5x_0 + 12x_1 + 2x_2 \geq 8$
- $7x_1 + 3x_2 \geq 21$
- $3x_0 + 7x_1 \geq 13$
- $3x_0 + 7x_1 + 3x_2 \geq 20$
- $5x_1 - 8x_2 \geq 0$
- $8x_0 - x_2 \geq 0$
- $5x_0 + 2x_2 \leq 59$
- $3x_0 + 3x_2 \leq 96$
- $x_0$ is an integer
- $x_1$ is continuous
- $x_2$ is continuous

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x0', 'milligrams of vitamin E'), ('x1', 'grams of protein'), ('x2', 'milligrams of zinc')],
    'objective_function': '7.61*x0 + 3.07*x1 + 6.17*x2',
    'constraints': [
        '3*x0 <= 91',
        '5*x0 <= 66',
        '3*x0 <= 102',
        '14*x1 <= 91',
        '12*x1 <= 66',
        '7*x1 <= 102',
        '14*x2 <= 91',
        '2*x2 <= 66',
        '3*x2 <= 102',
        '3*x0 + 14*x2 >= 24',
        '14*x1 + 14*x2 >= 30',
        '3*x0 + 14*x1 >= 17',
        '3*x0 + 14*x1 + 14*x2 >= 17',
        '5*x0 + 2*x2 >= 8',
        '5*x0 + 12*x1 + 2*x2 >= 8',
        '7*x1 + 3*x2 >= 21',
        '3*x0 + 7*x1 >= 13',
        '3*x0 + 7*x1 + 3*x2 >= 20',
        '5*x1 - 8*x2 >= 0',
        '8*x0 - x2 >= 0',
        '5*x0 + 2*x2 <= 59',
        '3*x0 + 3*x2 <= 96'
    ]
}
```

## Step 6: Write the Gurobi code in Python
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name='x0', vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin E
    x1 = model.addVar(name='x1')  # grams of protein
    x2 = model.addVar(name='x2')  # milligrams of zinc

    # Define the objective function
    model.setObjective(7.61 * x0 + 3.07 * x1 + 6.17 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(3 * x0 <= 91)
    model.addConstr(5 * x0 <= 66)
    model.addConstr(3 * x0 <= 102)
    model.addConstr(14 * x1 <= 91)
    model.addConstr(12 * x1 <= 66)
    model.addConstr(7 * x1 <= 102)
    model.addConstr(14 * x2 <= 91)
    model.addConstr(2 * x2 <= 66)
    model.addConstr(3 * x2 <= 102)
    model.addConstr(3 * x0 + 14 * x2 >= 24)
    model.addConstr(14 * x1 + 14 * x2 >= 30)
    model.addConstr(3 * x0 + 14 * x1 >= 17)
    model.addConstr(3 * x0 + 14 * x1 + 14 * x2 >= 17)
    model.addConstr(5 * x0 + 2 * x2 >= 8)
    model.addConstr(5 * x0 + 12 * x1 + 2 * x2 >= 8)
    model.addConstr(7 * x1 + 3 * x2 >= 21)
    model.addConstr(3 * x0 + 7 * x1 >= 13)
    model.addConstr(3 * x0 + 7 * x1 + 3 * x2 >= 20)
    model.addConstr(5 * x1 - 8 * x2 >= 0)
    model.addConstr(8 * x0 - x2 >= 0)
    model.addConstr(5 * x0 + 2 * x2 <= 59)
    model.addConstr(3 * x0 + 3 * x2 <= 96)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'milligrams of vitamin E: {x0.varValue}')
        print(f'grams of protein: {x1.varValue}')
        print(f'milligrams of zinc: {x2.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```