```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B1"),
    ("x1", "milligrams of vitamin B7"),
    ("x2", "grams of carbohydrates"),
    ("x3", "grams of fiber")
  ],
  "objective_function": "8*x0 + 1*x1 + 1*x2 + 2*x3",
  "constraints": [
    "1.31*x0 + 0.98*x1 + 0.33*x2 + 1.43*x3 >= 44",
    "0.98*x1 + 0.33*x2 >= 44",
    "0.85*x0 + 1.8*x2 >= 60",
    "1.8*x2 + 1.16*x3 >= 49",
    "0.85*x0 + 1.16*x3 >= 56",
    "0.85*x0 + 0.04*x1 + 1.8*x2 + 1.16*x3 >= 56",
    "9*x2 - 9*x3 >= 0",
    "-2*x1 + 9*x3 >= 0",
    "1.31*x0 + 0.33*x2 <= 76",
    "0.33*x2 + 1.43*x3 <= 191",
    "1.31*x0 <= 210",
    "0.85*x0 <= 243",
    "0.98*x1 <= 210",
    "0.04*x1 <= 243",
    "0.33*x2 <= 210",
    "1.8*x2 <= 243",
    "1.43*x3 <= 210",
    "1.16*x3 <= 243"

  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x0 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="vitamin_B1")
    x1 = m.addVar(vtype=gp.GRB.INTEGER, name="vitamin_B7")
    x2 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="carbohydrates")
    x3 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="fiber")

    # Set objective function
    m.setObjective(8*x0 + x1 + x2 + 2*x3, gp.GRB.MINIMIZE)

    # Add constraints
    m.addConstr(1.31*x0 + 0.98*x1 + 0.33*x2 + 1.43*x3 >= 44, "kidney_support_total")
    m.addConstr(0.98*x1 + 0.33*x2 >= 44, "kidney_support_B7_carbs")
    m.addConstr(0.85*x0 + 1.8*x2 >= 60, "muscle_growth_B1_carbs")
    m.addConstr(1.8*x2 + 1.16*x3 >= 49, "muscle_growth_carbs_fiber")
    m.addConstr(0.85*x0 + 1.16*x3 >= 56, "muscle_growth_B1_fiber")
    m.addConstr(0.85*x0 + 0.04*x1 + 1.8*x2 + 1.16*x3 >= 56, "muscle_growth_total")
    m.addConstr(9*x2 - 9*x3 >= 0, "carbs_fiber_ratio")
    m.addConstr(-2*x1 + 9*x3 >= 0, "B7_fiber_ratio")
    m.addConstr(1.31*x0 + 0.33*x2 <= 76, "kidney_support_B1_carbs_max")
    m.addConstr(0.33*x2 + 1.43*x3 <= 191, "kidney_support_carbs_fiber_max")


    # Add upper bound constraints based on resource attributes
    m.addConstr(1.31*x0 <= 210, "kidney_support_B1_max")
    m.addConstr(0.85*x0 <= 243, "muscle_growth_B1_max")
    m.addConstr(0.98*x1 <= 210, "kidney_support_B7_max")
    m.addConstr(0.04*x1 <= 243, "muscle_growth_B7_max")
    m.addConstr(0.33*x2 <= 210, "kidney_support_carbs_max_individual")
    m.addConstr(1.8*x2 <= 243, "muscle_growth_carbs_max_individual")
    m.addConstr(1.43*x3 <= 210, "kidney_support_fiber_max_individual")
    m.addConstr(1.16*x3 <= 243, "muscle_growth_fiber_max_individual")



    # 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.')


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

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