```json
{
  "sym_variables": [
    ("x0", "protein bars"),
    ("x1", "steaks"),
    ("x2", "ravioli"),
    ("x3", "knishes")
  ],
  "objective_function": "3.09*x0 + 9.24*x1 + 3.08*x2 + 1.7*x3",
  "constraints": [
    "3*x2 + 2*x3 >= 28",
    "8*x0 + 3*x2 >= 14",
    "8*x0 + 4*x1 + 3*x2 + 2*x3 >= 14",
    "9*x0 - 5*x3 >= 0",
    "-10*x2 + 10*x3 >= 0"
  ]
}
```

```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
protein_bars = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="protein_bars")
steaks = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="steaks")
ravioli = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="ravioli")
knishes = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="knishes")


# Set objective function
m.setObjective(3.09 * protein_bars + 9.24 * steaks + 3.08 * ravioli + 1.7 * knishes, GRB.MINIMIZE)

# Add constraints
m.addConstr(3 * ravioli + 2 * knishes >= 28, "protein_constraint1")
m.addConstr(8 * protein_bars + 3 * ravioli >= 14, "protein_constraint2")
m.addConstr(8 * protein_bars + 4 * steaks + 3 * ravioli + 2 * knishes >= 14, "protein_constraint3")
m.addConstr(9 * protein_bars - 5 * knishes >= 0, "constraint4")
m.addConstr(-10 * ravioli + 10 * knishes >= 0, "constraint5")



# Optimize model
m.optimize()

if m.status == GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('protein_bars:', protein_bars.x)
    print('steaks:', steaks.x)
    print('ravioli:', ravioli.x)
    print('knishes:', knishes.x)
elif m.status == GRB.INFEASIBLE:
    print('The model is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```
