## 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:
- $x_0$ represents 'milligrams of vitamin B3'
- $x_1$ represents 'milligrams of vitamin A'
- $x_2$ represents 'milligrams of iron'
- $x_3$ represents 'milligrams of vitamin D'

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $7x_0 + 4x_1 + 7x_2 + 8x_3$.

## 3: List the constraints in symbolic notation
Constraints:
1. $1.04x_0 \leq 123$
2. $0.27x_0 \leq 137$
3. $0.21x_1 \leq 123$
4. $1.39x_1 \leq 137$
5. $1.01x_2 \leq 123$
6. $0.87x_2 \leq 137$
7. $0.23x_3 \leq 123$
8. $1.64x_3 \leq 137$
9. $1.04x_0 + 1.01x_2 \geq 12$
10. $0.21x_1 + 1.01x_2 \geq 14$
11. $1.04x_0 + 0.21x_1 \geq 15$
12. $0.21x_1 + 0.23x_3 \geq 30$
13. $1.04x_0 + 0.21x_1 + 0.23x_3 \geq 27$
14. $0.87x_2 + 1.64x_3 \geq 15$
15. $0.27x_0 + 1.64x_3 \geq 33$
16. $1.39x_1 + 1.64x_3 \geq 19$
17. $1.39x_1 + 0.87x_2 \geq 24$
18. $0.27x_0 + 1.39x_1 \geq 15$
19. $0.27x_0 + 1.39x_1 + 1.64x_3 \geq 30$
20. $0.27x_0 + 0.87x_2 + 1.64x_3 \geq 30$
21. $1.39x_1 + 0.87x_2 \geq 30$
22. $0.27x_0 + 1.39x_1 + 1.64x_3 \geq 34$
23. $0.27x_0 + 0.87x_2 + 1.64x_3 \geq 34$
24. $0.27x_0 + 1.39x_1 + 0.87x_2 \geq 34$
25. $0.27x_0 + 1.39x_1 + 1.64x_3 \geq 33$
26. $0.27x_0 + 0.87x_2 + 1.64x_3 \geq 33$
27. $1.39x_1 + 0.87x_2 \geq 33$
28. $0.21x_1 + 1.01x_2 \leq 40$
29. $0.21x_1 + 0.23x_3 \leq 110$
30. $1.04x_0 + 1.01x_2 \leq 58$
31. $1.04x_0 + 0.21x_1 + 1.01x_2 + 0.23x_3 \leq 58$
32. $0.27x_0 + 0.87x_2 \leq 108$
33. $1.39x_1 + 1.64x_3 \leq 60$
34. $0.27x_0 + 1.39x_1 \leq 53$
35. $1.39x_1 + 0.87x_2 \leq 53$
36. $0.87x_2 + 1.64x_3 \leq 93$
37. $0.27x_0 + 0.87x_2 + 1.64x_3 \leq 46$
38. $0.27x_0 + 1.39x_1 + 0.87x_2 + 1.64x_3 \leq 46$

## 4: Define the symbolic representation of the problem in JSON format
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B3'),
        ('x1', 'milligrams of vitamin A'),
        ('x2', 'milligrams of iron'),
        ('x3', 'milligrams of vitamin D')
    ],
    'objective_function': '7*x0 + 4*x1 + 7*x2 + 8*x3',
    'constraints': [
        '1.04*x0 <= 123',
        '0.27*x0 <= 137',
        '0.21*x1 <= 123',
        '1.39*x1 <= 137',
        '1.01*x2 <= 123',
        '0.87*x2 <= 137',
        '0.23*x3 <= 123',
        '1.64*x3 <= 137',
        '1.04*x0 + 1.01*x2 >= 12',
        '0.21*x1 + 1.01*x2 >= 14',
        '1.04*x0 + 0.21*x1 >= 15',
        '0.21*x1 + 0.23*x3 >= 30',
        '1.04*x0 + 0.21*x1 + 0.23*x3 >= 27',
        '0.87*x2 + 1.64*x3 >= 15',
        '0.27*x0 + 1.64*x3 >= 33',
        '1.39*x1 + 1.64*x3 >= 19',
        '1.39*x1 + 0.87*x2 >= 24',
        '0.27*x0 + 1.39*x1 >= 15',
        '0.27*x0 + 1.39*x1 + 1.64*x3 >= 30',
        '0.27*x0 + 0.87*x2 + 1.64*x3 >= 30',
        '1.39*x1 + 0.87*x2 >= 30',
        '0.27*x0 + 1.39*x1 + 1.64*x3 >= 34',
        '0.27*x0 + 0.87*x2 + 1.64*x3 >= 34',
        '0.27*x0 + 1.39*x1 + 0.87*x2 >= 34',
        '0.27*x0 + 1.39*x1 + 1.64*x3 >= 33',
        '0.27*x0 + 0.87*x2 + 1.64*x3 >= 33',
        '1.39*x1 + 0.87*x2 >= 33',
        '0.21*x1 + 1.01*x2 <= 40',
        '0.21*x1 + 0.23*x3 <= 110',
        '1.04*x0 + 1.01*x2 <= 58',
        '1.04*x0 + 0.21*x1 + 1.01*x2 + 0.23*x3 <= 58',
        '0.27*x0 + 0.87*x2 <= 108',
        '1.39*x1 + 1.64*x3 <= 60',
        '0.27*x0 + 1.39*x1 <= 53',
        '1.39*x1 + 0.87*x2 <= 53',
        '0.87*x2 + 1.64*x3 <= 93',
        '0.27*x0 + 0.87*x2 + 1.64*x3 <= 46',
        '0.27*x0 + 1.39*x1 + 0.87*x2 + 1.64*x3 <= 46'
    ]
}
```

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

def optimize_problem():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(name="x0", vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin B3
    x1 = model.addVar(name="x1", vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin A
    x2 = model.addVar(name="x2")  # milligrams of iron
    x3 = model.addVar(name="x3")  # milligrams of vitamin D

    # Objective function
    model.setObjective(7 * x0 + 4 * x1 + 7 * x2 + 8 * x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(1.04 * x0 <= 123)
    model.addConstr(0.27 * x0 <= 137)
    model.addConstr(0.21 * x1 <= 123)
    model.addConstr(1.39 * x1 <= 137)
    model.addConstr(1.01 * x2 <= 123)
    model.addConstr(0.87 * x2 <= 137)
    model.addConstr(0.23 * x3 <= 123)
    model.addConstr(1.64 * x3 <= 137)
    model.addConstr(1.04 * x0 + 1.01 * x2 >= 12)
    model.addConstr(0.21 * x1 + 1.01 * x2 >= 14)
    model.addConstr(1.04 * x0 + 0.21 * x1 >= 15)
    model.addConstr(0.21 * x1 + 0.23 * x3 >= 30)
    model.addConstr(1.04 * x0 + 0.21 * x1 + 0.23 * x3 >= 27)
    model.addConstr(0.87 * x2 + 1.64 * x3 >= 15)
    model.addConstr(0.27 * x0 + 1.64 * x3 >= 33)
    model.addConstr(1.39 * x1 + 1.64 * x3 >= 19)
    model.addConstr(1.39 * x1 + 0.87 * x2 >= 24)
    model.addConstr(0.27 * x0 + 1.39 * x1 >= 15)
    model.addConstr(0.27 * x0 + 1.39 * x1 + 1.64 * x3 >= 30)
    model.addConstr(0.27 * x0 + 0.87 * x2 + 1.64 * x3 >= 30)
    model.addConstr(1.39 * x1 + 0.87 * x2 >= 30)
    model.addConstr(0.27 * x0 + 1.39 * x1 + 1.64 * x3 >= 34)
    model.addConstr(0.27 * x0 + 0.87 * x2 + 1.64 * x3 >= 34)
    model.addConstr(0.27 * x0 + 1.39 * x1 + 0.87 * x2 >= 34)
    model.addConstr(0.27 * x0 + 1.39 * x1 + 1.64 * x3 >= 33)
    model.addConstr(0.27 * x0 + 0.87 * x2 + 1.64 * x3 >= 33)
    model.addConstr(1.39 * x1 + 0.87 * x2 >= 33)
    model.addConstr(0.21 * x1 + 1.01 * x2 <= 40)
    model.addConstr(0.21 * x1 + 0.23 * x3 <= 110)
    model.addConstr(1.04 * x0 + 1.01 * x2 <= 58)
    model.addConstr(1.04 * x0 + 0.21 * x1 + 1.01 * x2 + 0.23 * x3 <= 58)
    model.addConstr(0.27 * x0 + 0.87 * x2 <= 108)
    model.addConstr(1.39 * x1 + 1.64 * x3 <= 60)
    model.addConstr(0.27 * x0 + 1.39 * x1 <= 53)
    model.addConstr(1.39 * x1 + 0.87 * x2 <= 53)
    model.addConstr(0.87 * x2 + 1.64 * x3 <= 93)
    model.addConstr(0.27 * x0 + 0.87 * x2 + 1.64 * x3 <= 46)
    model.addConstr(0.27 * x0 + 1.39 * x1 + 0.87 * x2 + 1.64 * x3 <= 46)

    # 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)
        print("x3: ", x3.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```