```json
{
  "sym_variables": [
    ("x0", "milligrams of magnesium"),
    ("x1", "milligrams of vitamin B2"),
    ("x2", "milligrams of vitamin C"),
    ("x3", "milligrams of vitamin B6")
  ],
  "objective_function": "5.28 * x0 + 4.71 * x1 + 8.96 * x2 + 6.81 * x3",
  "constraints": [
    "4.9 * x0 + 4.7 * x2 >= 37",
    "4.36 * x1 + 4.7 * x2 >= 26",
    "3.03 * x1 + 4.5 * x2 >= 36",
    "4.9 * x0 + 4.36 * x1 <= 101",
    "4.9 * x0 + 5.86 * x3 <= 96",
    "4.7 * x2 + 5.86 * x3 <= 69",
    "4.36 * x1 + 5.86 * x3 <= 141",
    "4.9 * x0 + 4.7 * x2 + 5.86 * x3 <= 185",
    "4.9 * x0 + 4.36 * x1 + 5.86 * x3 <= 100",
    "4.36 * x1 + 4.7 * x2 + 5.86 * x3 <= 157",
    "4.9 * x0 + 4.36 * x1 + 4.7 * x2 + 5.86 * x3 <= 157",
    "5.9 * x0 + 1.5 * x3 <= 71",
    "4.5 * x2 + 1.5 * x3 <= 71",
    "3.03 * x1 + 1.5 * x3 <= 186",
    "5.9 * x0 + 3.03 * x1 <= 188",
    "3.03 * x1 + 4.5 * x2 <= 87",
    "5.9 * x0 + 4.5 * x2 <= 164",
    "5.9 * x0 + 3.03 * x1 + 1.5 * x3 <= 157",
    "5.9 * x0 + 3.03 * x1 + 4.5 * x2 <= 150",
    "5.9 * x0 + 3.03 * x1 + 4.5 * x2 + 1.5 * x3 <= 150",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
import gurobipy as gp

try:
    # Create a new model
    m = gp.Model("nutrition_optimization")

    # Create variables
    magnesium = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="magnesium")
    vitamin_b2 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b2")
    vitamin_c = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_c")
    vitamin_b6 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b6")

    # Set objective function
    m.setObjective(5.28 * magnesium + 4.71 * vitamin_b2 + 8.96 * vitamin_c + 6.81 * vitamin_b6, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(4.9 * magnesium + 4.7 * vitamin_c >= 37)
    m.addConstr(4.36 * vitamin_b2 + 4.7 * vitamin_c >= 26)
    m.addConstr(3.03 * vitamin_b2 + 4.5 * vitamin_c >= 36)
    m.addConstr(4.9 * magnesium + 4.36 * vitamin_b2 <= 101)
    m.addConstr(4.9 * magnesium + 5.86 * vitamin_b6 <= 96)
    m.addConstr(4.7 * vitamin_c + 5.86 * vitamin_b6 <= 69)
    m.addConstr(4.36 * vitamin_b2 + 5.86 * vitamin_b6 <= 141)
    m.addConstr(4.9 * magnesium + 4.7 * vitamin_c + 5.86 * vitamin_b6 <= 185)
    m.addConstr(4.9 * magnesium + 4.36 * vitamin_b2 + 5.86 * vitamin_b6 <= 100)
    m.addConstr(4.36 * vitamin_b2 + 4.7 * vitamin_c + 5.86 * vitamin_b6 <= 157)
    m.addConstr(4.9 * magnesium + 4.36 * vitamin_b2 + 4.7 * vitamin_c + 5.86 * vitamin_b6 <= 157)
    m.addConstr(5.9 * magnesium + 1.5 * vitamin_b6 <= 71)
    m.addConstr(4.5 * vitamin_c + 1.5 * vitamin_b6 <= 71)
    m.addConstr(3.03 * vitamin_b2 + 1.5 * vitamin_b6 <= 186)
    m.addConstr(5.9 * magnesium + 3.03 * vitamin_b2 <= 188)
    m.addConstr(3.03 * vitamin_b2 + 4.5 * vitamin_c <= 87)
    m.addConstr(5.9 * magnesium + 4.5 * vitamin_c <= 164)
    m.addConstr(5.9 * magnesium + 3.03 * vitamin_b2 + 1.5 * vitamin_b6 <= 157)
    m.addConstr(5.9 * magnesium + 3.03 * vitamin_b2 + 4.5 * vitamin_c <= 150)
    m.addConstr(5.9 * magnesium + 3.03 * vitamin_b2 + 4.5 * vitamin_c + 1.5 * vitamin_b6 <= 150)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        for v in m.getVars():
            print('%s %g' % (v.varName, v.x))
    elif m.status == gp.GRB.INFEASIBLE:
        print('Optimization problem is infeasible.')
    else:
        print('Optimization ended with status %d' % m.status)


except gp.GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

except AttributeError:
    print('Encountered an attribute error')
```
