## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B5', 'milligrams of magnesium', 'milligrams of potassium', 'milligrams of zinc']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin B5
- $x_1$ : milligrams of magnesium
- $x_2$ : milligrams of potassium
- $x_3$ : milligrams of zinc

## 3: Define the objective function in symbolic notation
The objective function to minimize is $2x_0 + 3x_1 + x_2 + 2x_3$.

## 4: List the constraints in symbolic notation
Constraints:
1. $3x_1 + 8x_2 + 7x_3 \geq 36$
2. $8x_2 + 13x_3 \geq 16$
3. $3x_1 + 7x_3 \geq 20$
4. $6x_0 + 3x_1 \geq 27$
5. $6x_0 + 3x_1 + 7x_3 \geq 35$
6. $6x_0 + 3x_1 + 8x_2 + 7x_3 \geq 35$
7. $6x_0 + 13x_3 \geq 29$
8. $8x_1 + 13x_3 \geq 22$
9. $8x_1 + 5x_2 \geq 34$
10. $6x_0 + 5x_2 + 13x_3 \geq 37$
11. $6x_0 + 8x_1 + 5x_2 \geq 37$
12. $6x_0 + 5x_2 + 13x_3 \geq 30$
13. $6x_0 + 8x_1 + 5x_2 \geq 30$
14. $6x_0 + 8x_1 + 5x_2 + 13x_3 \geq 30$
15. $7x_2 + 8x_3 \geq 29$
16. $x_1 + 8x_3 \geq 34$
17. $10x_0 + 7x_2 + 8x_3 \geq 35$
18. $10x_0 + x_1 + 7x_2 \geq 35$
19. $10x_0 + 7x_2 + 8x_3 \geq 21$
20. $10x_0 + x_1 + 7x_2 \geq 21$
21. $10x_0 + x_1 + 7x_2 + 8x_3 \geq 21$
22. $4x_1 - 5x_3 \geq 0$
23. $9x_0 - 9x_1 \geq 0$
24. $3x_1 + 7x_3 \leq 137$
25. $6x_0 + 3x_1 \leq 112$
26. $3x_1 + 8x_2 \leq 138$
27. $6x_0 + 8x_2 \leq 127$
28. $6x_0 + 7x_3 \leq 128$
29. $6x_0 + 8x_2 + 7x_3 \leq 61$
30. $6x_0 + 3x_1 + 8x_2 \leq 51$
31. $6x_0 + 5x_2 \leq 116$
32. $8x_1 + 5x_2 \leq 140$
33. $5x_2 + 13x_3 \leq 88$
34. $10x_0 + 7x_2 + 8x_3 \leq 140$
35. $10x_0 + x_1 + 7x_2 \leq 132$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B5'),
        ('x1', 'milligrams of magnesium'),
        ('x2', 'milligrams of potassium'),
        ('x3', 'milligrams of zinc')
    ],
    'objective_function': '2*x0 + 3*x1 + x2 + 2*x3',
    'constraints': [
        '3*x1 + 8*x2 + 7*x3 >= 36',
        '8*x2 + 13*x3 >= 16',
        '3*x1 + 7*x3 >= 20',
        '6*x0 + 3*x1 >= 27',
        '6*x0 + 3*x1 + 7*x3 >= 35',
        '6*x0 + 3*x1 + 8*x2 + 7*x3 >= 35',
        '6*x0 + 13*x3 >= 29',
        '8*x1 + 13*x3 >= 22',
        '8*x1 + 5*x2 >= 34',
        '6*x0 + 5*x2 + 13*x3 >= 37',
        '6*x0 + 8*x1 + 5*x2 >= 37',
        '6*x0 + 5*x2 + 13*x3 >= 30',
        '6*x0 + 8*x1 + 5*x2 >= 30',
        '6*x0 + 8*x1 + 5*x2 + 13*x3 >= 30',
        '7*x2 + 8*x3 >= 29',
        'x1 + 8*x3 >= 34',
        '10*x0 + 7*x2 + 8*x3 >= 35',
        '10*x0 + x1 + 7*x2 >= 35',
        '10*x0 + 7*x2 + 8*x3 >= 21',
        '10*x0 + x1 + 7*x2 >= 21',
        '10*x0 + x1 + 7*x2 + 8*x3 >= 21',
        '4*x1 - 5*x3 >= 0',
        '9*x0 - 9*x1 >= 0',
        '3*x1 + 7*x3 <= 137',
        '6*x0 + 3*x1 <= 112',
        '3*x1 + 8*x2 <= 138',
        '6*x0 + 8*x2 <= 127',
        '6*x0 + 7*x3 <= 128',
        '6*x0 + 8*x2 + 7*x3 <= 61',
        '6*x0 + 3*x1 + 8*x2 <= 51',
        '6*x0 + 5*x2 <= 116',
        '8*x1 + 5*x2 <= 140',
        '5*x2 + 13*x3 <= 88',
        '10*x0 + 7*x2 + 8*x3 <= 140',
        '10*x0 + x1 + 7*x2 <= 132'
    ]
}
```

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

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

    # Define variables
    x0 = model.addVar(name="x0", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B5
    x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of magnesium
    x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of potassium
    x3 = model.addVar(name="x3", lb=-gurobi.GRB.INFINITY)  # milligrams of zinc

    # Objective function
    model.setObjective(2 * x0 + 3 * x1 + x2 + 2 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(3 * x1 + 8 * x2 + 7 * x3 >= 36)
    model.addConstr(8 * x2 + 13 * x3 >= 16)
    model.addConstr(3 * x1 + 7 * x3 >= 20)
    model.addConstr(6 * x0 + 3 * x1 >= 27)
    model.addConstr(6 * x0 + 3 * x1 + 7 * x3 >= 35)
    model.addConstr(6 * x0 + 3 * x1 + 8 * x2 + 7 * x3 >= 35)
    model.addConstr(6 * x0 + 13 * x3 >= 29)
    model.addConstr(8 * x1 + 13 * x3 >= 22)
    model.addConstr(8 * x1 + 5 * x2 >= 34)
    model.addConstr(6 * x0 + 5 * x2 + 13 * x3 >= 37)
    model.addConstr(6 * x0 + 8 * x1 + 5 * x2 >= 37)
    model.addConstr(6 * x0 + 5 * x2 + 13 * x3 >= 30)
    model.addConstr(6 * x0 + 8 * x1 + 5 * x2 >= 30)
    model.addConstr(6 * x0 + 8 * x1 + 5 * x2 + 13 * x3 >= 30)
    model.addConstr(7 * x2 + 8 * x3 >= 29)
    model.addConstr(x1 + 8 * x3 >= 34)
    model.addConstr(10 * x0 + 7 * x2 + 8 * x3 >= 35)
    model.addConstr(10 * x0 + x1 + 7 * x2 >= 35)
    model.addConstr(10 * x0 + 7 * x2 + 8 * x3 >= 21)
    model.addConstr(10 * x0 + x1 + 7 * x2 >= 21)
    model.addConstr(10 * x0 + x1 + 7 * x2 + 8 * x3 >= 21)
    model.addConstr(4 * x1 - 5 * x3 >= 0)
    model.addConstr(9 * x0 - 9 * x1 >= 0)
    model.addConstr(3 * x1 + 7 * x3 <= 137)
    model.addConstr(6 * x0 + 3 * x1 <= 112)
    model.addConstr(3 * x1 + 8 * x2 <= 138)
    model.addConstr(6 * x0 + 8 * x2 <= 127)
    model.addConstr(6 * x0 + 7 * x3 <= 128)
    model.addConstr(6 * x0 + 8 * x2 + 7 * x3 <= 61)
    model.addConstr(6 * x0 + 3 * x1 + 8 * x2 <= 51)
    model.addConstr(6 * x0 + 5 * x2 <= 116)
    model.addConstr(8 * x1 + 5 * x2 <= 140)
    model.addConstr(5 * x2 + 13 * x3 <= 88)
    model.addConstr(10 * x0 + 7 * x2 + 8 * x3 <= 140)
    model.addConstr(10 * x0 + x1 + 7 * x2 <= 132)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x0: {x0.varValue}")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"x3: {x3.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```