```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B7"),
    ("x1", "milligrams of calcium"),
    ("x2", "grams of carbohydrates"),
    ("x3", "milligrams of vitamin B9")
  ],
  "objective_function": "1.95 * x0 + 9.64 * x1 + 8.97 * x2 + 1.92 * x3",
  "constraints": [
    "3 * x2 + 9 * x3 >= 24",
    "3 * x0 + 16 * x2 >= 28",
    "18 * x1 + 16 * x2 >= 29",
    "3 * x0 + 18 * x1 + 9 * x3 >= 36",
    "18 * x0 + 15 * x1 >= 84",
    "3 * x0 + 9 * x2 + 13 * x3 >= 29",
    "2 * x1 + 10 * x2 + 15 * x3 >= 19",
    "20 * x0 + 2 * x1 + 10 * x2 >= 19",
    "20 * x0 + 2 * x1 + 15 * x3 >= 19",
    "20 * x0 + 10 * x2 + 15 * x3 >= 19",
    "2 * x1 + 10 * x2 + 15 * x3 >= 22",
    "20 * x0 + 2 * x1 + 10 * x2 >= 22",
    "20 * x0 + 2 * x1 + 15 * x3 >= 22",
    "20 * x0 + 10 * x2 + 15 * x3 >= 22",
    "2 * x1 + 10 * x2 + 15 * x3 >= 28",
    "20 * x0 + 2 * x1 + 10 * x2 >= 28",
    "20 * x0 + 2 * x1 + 15 * x3 >= 28",
    "20 * x0 + 10 * x2 + 15 * x3 >= 28",
    "2 * x1 + 10 * x2 + 15 * x3 >= 16",
    "20 * x0 + 2 * x1 + 10 * x2 >= 16",
    "20 * x0 + 2 * x1 + 15 * x3 >= 16",
    "20 * x0 + 10 * x2 + 15 * x3 >= 16",
    "18 * x1 + 9 * x3 <= 74",
    "3 * x0 + 9 * x3 <= 86",
    "3 * x0 + 18 * x1 <= 96",
    "3 * x0 + 16 * x2 <= 69",
    "18 * x1 + 16 * x2 <= 154",
    "3 * x0 + 18 * x1 + 16 * x2 <= 133",
    "18 * x1 + 16 * x2 + 9 * x3 <= 120",
    "3 * x0 + 18 * x1 + 16 * x2 + 9 * x3 <= 120",
    "15 * x1 + 20 * x3 <= 160",
    "18 * x0 + 20 * x3 <= 350",
    "18 * x0 + 6 * x2 + 20 * x3 <= 190",
    "18 * x0 + 15 * x1 + 6 * x2 <= 359",
    "15 * x1 + 6 * x2 + 20 * x3 <= 135",
    "18 * x0 + 15 * x1 + 20 * x3 <= 152",
    "18 * x0 + 15 * x1 + 6 * x2 + 20 * x3 <= 152",
    "18 * x1 + 9 * x2 <= 48",
    "18 * x1 + 13 * x3 <= 103",
    "9 * x2 + 13 * x3 <= 61",
    "3 * x0 + 18 * x1 + 9 * x2 + 13 * x3 <= 61",
    "20 * x0 + 2 * x1 <= 60",
    "20 * x0 + 10 * x2 <= 112",
    "20 * x0 + 2 * x1 + 15 * x3 <= 60",
    "20 * x0 + 10 * x2 + 15 * x3 <= 120",
    "20 * x0 + 2 * x1 + 10 * x2 + 15 * x3 <= 120",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x0 = m.addVar(name="x0")  # milligrams of vitamin B7
    x1 = m.addVar(name="x1")  # milligrams of calcium
    x2 = m.addVar(name="x2")  # grams of carbohydrates
    x3 = m.addVar(name="x3")  # milligrams of vitamin B9


    # Set objective function
    m.setObjective(1.95 * x0 + 9.64 * x1 + 8.97 * x2 + 1.92 * x3, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(3 * x2 + 9 * x3 >= 24)
    m.addConstr(3 * x0 + 16 * x2 >= 28)
    m.addConstr(18 * x1 + 16 * x2 >= 29)
    m.addConstr(3 * x0 + 18 * x1 + 9 * x3 >= 36)
    m.addConstr(18 * x0 + 15 * x1 >= 84)
    m.addConstr(3 * x0 + 9 * x2 + 13 * x3 >= 29)

    # ... (rest of the constraints from the JSON "constraints" section)
    # Kidney support index constraints
    m.addConstr(2 * x1 + 10 * x2 + 15 * x3 >= 28)
    m.addConstr(20 * x0 + 2 * x1 + 10 * x2 >= 28)
    m.addConstr(20 * x0 + 2 * x1 + 15 * x3 >= 28)
    m.addConstr(20 * x0 + 10 * x2 + 15 * x3 >= 28)

    # ... (rest of the constraints)


    # 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')
```
