## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_1$ represents the milligrams of vitamin B3
- $x_2$ represents the milligrams of vitamin B2
- $x_3$ represents the milligrams of magnesium

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $8.18x_1 + 7.28x_2 + 9.58x_3$.

## 3: List the constraints in symbolic notation
1. $0.58x_1 + 2.19x_3 \geq 33$
2. $0.58x_1 + 2.05x_2 + 2.19x_3 \geq 30$
3. $2.81x_1 + 0.07x_3 \geq 59$
4. $1.34x_2 + 0.07x_3 \geq 36$
5. $2.58x_1 + 0.1x_2 \geq 24$
6. $0.1x_2 + 0.1x_3 \geq 58$
7. $2.58x_1 + 0.1x_3 \geq 67$
8. $2.05x_2 + 2.19x_3 \leq 69$
9. $0.58x_1 + 2.19x_3 \leq 53$
10. $0.58x_1 + 2.05x_2 + 2.19x_3 \leq 117$
11. $1.34x_2 + 0.07x_3 \leq 189$
12. $2.81x_1 + 1.34x_2 \leq 182$
13. $2.81x_1 + 0.07x_3 \leq 155$
14. $2.81x_1 + 1.34x_2 + 0.07x_3 \leq 153$
15. $0.1x_2 + 0.1x_3 \leq 186$
16. $2.58x_1 + 0.1x_3 \leq 87$
17. $2.58x_1 + 0.1x_2 + 0.1x_3 \leq 87$
18. $1.05x_1 + 2.59x_2 \leq 157$
19. $2.59x_2 + 1.29x_3 \leq 96$
20. $1.05x_1 + 2.59x_2 + 1.29x_3 \leq 96$

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B3'), ('x2', 'milligrams of vitamin B2'), ('x3', 'milligrams of magnesium')],
    'objective_function': '8.18*x1 + 7.28*x2 + 9.58*x3',
    'constraints': [
        '0.58*x1 + 2.19*x3 >= 33',
        '0.58*x1 + 2.05*x2 + 2.19*x3 >= 30',
        '2.81*x1 + 0.07*x3 >= 59',
        '1.34*x2 + 0.07*x3 >= 36',
        '2.58*x1 + 0.1*x2 >= 24',
        '0.1*x2 + 0.1*x3 >= 58',
        '2.58*x1 + 0.1*x3 >= 67',
        '2.05*x2 + 2.19*x3 <= 69',
        '0.58*x1 + 2.19*x3 <= 53',
        '0.58*x1 + 2.05*x2 + 2.19*x3 <= 117',
        '1.34*x2 + 0.07*x3 <= 189',
        '2.81*x1 + 1.34*x2 <= 182',
        '2.81*x1 + 0.07*x3 <= 155',
        '2.81*x1 + 1.34*x2 + 0.07*x3 <= 153',
        '0.1*x2 + 0.1*x3 <= 186',
        '2.58*x1 + 0.1*x3 <= 87',
        '2.58*x1 + 0.1*x2 + 0.1*x3 <= 87',
        '1.05*x1 + 2.59*x2 <= 157',
        '2.59*x2 + 1.29*x3 <= 96',
        '1.05*x1 + 2.59*x2 + 1.29*x3 <= 96'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B3
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B2
    x3 = model.addVar(name="x3", lb=0)  # milligrams of magnesium

    # Define the objective function
    model.setObjective(8.18 * x1 + 7.28 * x2 + 9.58 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(0.58 * x1 + 2.19 * x3 >= 33)
    model.addConstr(0.58 * x1 + 2.05 * x2 + 2.19 * x3 >= 30)
    model.addConstr(2.81 * x1 + 0.07 * x3 >= 59)
    model.addConstr(1.34 * x2 + 0.07 * x3 >= 36)
    model.addConstr(2.58 * x1 + 0.1 * x2 >= 24)
    model.addConstr(0.1 * x2 + 0.1 * x3 >= 58)
    model.addConstr(2.58 * x1 + 0.1 * x3 >= 67)
    model.addConstr(2.05 * x2 + 2.19 * x3 <= 69)
    model.addConstr(0.58 * x1 + 2.19 * x3 <= 53)
    model.addConstr(0.58 * x1 + 2.05 * x2 + 2.19 * x3 <= 117)
    model.addConstr(1.34 * x2 + 0.07 * x3 <= 189)
    model.addConstr(2.81 * x1 + 1.34 * x2 <= 182)
    model.addConstr(2.81 * x1 + 0.07 * x3 <= 155)
    model.addConstr(2.81 * x1 + 1.34 * x2 + 0.07 * x3 <= 153)
    model.addConstr(0.1 * x2 + 0.1 * x3 <= 186)
    model.addConstr(2.58 * x1 + 0.1 * x3 <= 87)
    model.addConstr(2.58 * x1 + 0.1 * x2 + 0.1 * x3 <= 87)
    model.addConstr(1.05 * x1 + 2.59 * x2 <= 157)
    model.addConstr(2.59 * x2 + 1.29 * x3 <= 96)
    model.addConstr(1.05 * x1 + 2.59 * x2 + 1.29 * x3 <= 96)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B3: {x1.varValue}")
        print(f"Milligrams of vitamin B2: {x2.varValue}")
        print(f"Milligrams of magnesium: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```