```json
{
  "sym_variables": [
    ("x0", "chicken thighs"),
    ("x1", "bagged salads"),
    ("x2", "apples"),
    ("x3", "knishes"),
    ("x4", "slices of pizza")
  ],
  "objective_function": "7*x0 + 5*x1 + 5*x2 + 9*x3 + 3*x4",
  "constraints": [
    "2*x1 + 16*x3 >= 28",
    "2*x0 + 15*x2 >= 37",
    "15*x2 + 16*x3 >= 33",
    "2*x0 + 7*x4 >= 37",
    "16*x3 + 7*x4 >= 21",
    "2*x1 + 15*x2 >= 39",
    "2*x0 + 16*x3 + 7*x4 >= 29",
    "2*x0 + 2*x1 + 15*x2 + 16*x3 + 7*x4 >= 29",
    "2*x3 - 10*x4 >= 0",
    "15*x2 + 16*x3 <= 64",
    "2*x1 + 15*x2 + 16*x3 <= 196",
    "2*x0 + 2*x1 + 7*x4 <= 153",
    "2*x0 + 2*x1 + 15*x2 <= 86",
    "2*x0 + 2*x1 + 15*x2 + 16*x3 + 7*x4 <= 207" 
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x = m.addVars(5, lb=0, vtype=gp.GRB.CONTINUOUS, name=["chicken thighs", "bagged salads", "apples", "knishes", "slices of pizza"])

    # Set objective function
    m.setObjective(7*x[0] + 5*x[1] + 5*x[2] + 9*x[3] + 3*x[4], gp.GRB.MINIMIZE)

    # Add constraints
    m.addConstr(2*x[1] + 16*x[3] >= 28, "c1")
    m.addConstr(2*x[0] + 15*x[2] >= 37, "c2")
    m.addConstr(15*x[2] + 16*x[3] >= 33, "c3")
    m.addConstr(2*x[0] + 7*x[4] >= 37, "c4")
    m.addConstr(16*x[3] + 7*x[4] >= 21, "c5")
    m.addConstr(2*x[1] + 15*x[2] >= 39, "c6")
    m.addConstr(2*x[0] + 16*x[3] + 7*x[4] >= 29, "c7")
    m.addConstr(2*x[0] + 2*x[1] + 15*x[2] + 16*x[3] + 7*x[4] >= 29, "c8")
    m.addConstr(2*x[3] - 10*x[4] >= 0, "c9")
    m.addConstr(15*x[2] + 16*x[3] <= 64, "c10")
    m.addConstr(2*x[1] + 15*x[2] + 16*x[3] <= 196, "c11")
    m.addConstr(2*x[0] + 2*x[1] + 7*x[4] <= 153, "c12")
    m.addConstr(2*x[0] + 2*x[1] + 15*x[2] <= 86, "c13")
    m.addConstr(2*x[0] + 2*x[1] + 15*x[2] + 16*x[3] + 7*x[4] <= 207, "c14")


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        for v in m.getVars():
            print('%s %g' % (v.varName, v.x))
        print('Obj: %g' % m.objVal)
    elif m.status == gp.GRB.INFEASIBLE:
        print('The model 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')
```