```json
{
  "sym_variables": [
    ("x0", "blueberry pies"),
    ("x1", "apples"),
    ("x2", "chicken breasts"),
    ("x3", "green beans"),
    ("x4", "chicken thighs"),
    ("x5", "rotisserie chickens"),
    ("x6", "bagged salads")
  ],
  "objective_function": "4.69 * x0 * x1 + 4.68 * x0 * x5 + 7.01 * x1 * x3 + 9.9 * x2 * x3 + 3.66 * x2 * x5 + 1.95 * x1 + 3.88 * x6",
  "constraints": [
    "8 * x0 + 13 * x1 + 12 * x2 + 5 * x3 + 5 * x4 + 7 * x5 + 9 * x6 <= 222",
    "10 * x0 + 5 * x1 + 7 * x2 + 3 * x3 + 4 * x4 + 11 * x5 + 5 * x6 <= 181",
    "13 * x0 + 1 * x1 + 5 * x2 + 3 * x3 + 14 * x4 + 10 * x5 + 1 * x6 <= 195",
    "14 * x0 + 14 * x1 + 14 * x2 + 6 * x3 + 5 * x4 + 3 * x5 + 4 * x6 <= 169",
    "13 * x1 + 5 * x4 >= 14",
    "12 * x2 + 5 * x3 >= 18",
    "13 * x1**2 + 7 * x5**2 >= 18",
    "8 * x0 + 9 * x6 >= 16",
    "5 * x4 + 7 * x5 >= 19",
    "5 * x3 + 7 * x5 >= 22",
    "8 * x0 + 7 * x5 >= 31",
    "5 * x3**2 + 9 * x6**2 >= 21",
    "5 * x4 + 9 * x6 >= 27",
    "5 * x3**2 + 5 * x4**2 >= 31",
    "8 * x0**2 + 5 * x4**2 >= 31",
    "12 * x2 + 5 * x3 + 9 * x6 >= 28",
    "13 * x1 + 12 * x2 + 5 * x3 >= 28",
    "8 * x0**2 + 12 * x2**2 + 7 * x5**2 >= 28",
    "12 * x2 + 5 * x3 + 7 * x5 >= 28",
    "13 * x1 + 5 * x3 + 9 * x6 >= 28",
    "8 * x0**2 + 5 * x4**2 + 9 * x6**2 >= 28",
    "13 * x1 + 5 * x4 + 7 * x5 >= 28",
    "13 * x1 + 12 * x2 + 9 * x6 >= 28",
    "8 * x0**2 + 5 * x3**2 + 5 * x4**2 >= 28",
    "12 * x2**2 + 5 * x3**2 + 5 * x4**2 >= 28",
    "5 * x3 + 7 * x5 + 9 * x6 >= 28",
    "5 * x4 + 7 * x5 + 9 * x6 >= 28",
    "13 * x1 + 12 * x2 + 7 * x5 >= 28",
    "8 * x0 + 5 * x4 + 7 * x5 >= 28",
    "8 * x0 + 12 * x2 + 5 * x4 >= 28",
    "13 * x1 + 12 * x2 + 5 * x4 >= 28",
    "5 * x3**2 + 5 * x4**2 + 9 * x6**2 >= 28",
    "8 * x0 + 13 * x1 + 9 * x6 >= 28",
    "12 * x2 + 5 * x3 + 9 * x6 >= 31",
    "13 * x1**2 + 12 * x2**2 + 5 * x3**2 >= 31",
    "8 * x0 + 12 * x2 + 7 * x5 >= 31",
    "5 * x1 + 5 * x6 >= 14",
    "10 * x0 + 7 * x2 >= 15",
    "3 * x3 + 4 * x4 >= 21",
    "7 * x2 + 3 * x3 >= 20",
    "10 * x0**2 + 3 * x3**2 >= 18",
    "7 * x2 + 4 * x4 >= 20",
    "5 * x1 + 11 * x5 >= 15",
    "10 * x0 + 4 * x4 >= 15",
    "10 * x0 + 11 * x5 >= 15",
    "7 * x2**2 + 11 * x5**2 >= 10",
    "4 * x4 + 11 * x5 >= 8",
    "10 * x5 + x6 >= 20",
    "x1**2 + x4**2 >= 11",
    "x1 + x6 >= 24",
    "5 * x2 + 10 * x5 >= 24",
    "13 * x0**2 + x1**2 >= 25",
    "-7 * x0 + 3 * x1 >= 0",
    "3 * x2 - 4 * x6 >= 0",
    "4 * x2 - 7 * x4 + 3 * x5 >= 0",
    "9 * x1 - 3 * x2 - 4 * x4 >= 0",
    "13 * x1**2 + 12 * x2**2 + 7 * x5**2 <= 41"
    
    
    
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
x = m.addVars(7, lb=0.0, ub=gp.GRB.INFINITY, vtype=[gp.GRB.CONTINUOUS, gp.GRB.CONTINUOUS, gp.GRB.CONTINUOUS, gp.GRB.CONTINUOUS, gp.GRB.CONTINUOUS, gp.GRB.INTEGER, gp.GRB.CONTINUOUS], names=['blueberry_pies', 'apples', 'chicken_breasts', 'green_beans', 'chicken_thighs', 'rotisserie_chickens', 'bagged_salads'])


# Set objective function
m.setObjective(4.69 * x[0] * x[1] + 4.68 * x[0] * x[5] + 7.01 * x[1] * x[3] + 9.9 * x[2] * x[3] + 3.66 * x[2] * x[5] + 1.95 * x[1] + 3.88 * x[6], gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(8 * x[0] + 13 * x[1] + 12 * x[2] + 5 * x[3] + 5 * x[4] + 7 * x[5] + 9 * x[6] <= 222, "r0")
m.addConstr(10 * x[0] + 5 * x[1] + 7 * x[2] + 3 * x[3] + 4 * x[4] + 11 * x[5] + 5 * x[6] <= 181, "r1")
m.addConstr(13 * x[0] + 1 * x[1] + 5 * x[2] + 3 * x[3] + 14 * x[4] + 10 * x[5] + 1 * x[6] <= 195, "r2")
m.addConstr(14 * x[0] + 14 * x[1] + 14 * x[2] + 6 * x[3] + 5 * x[4] + 3 * x[5] + 4 * x[6] <= 169, "r3")
m.addConstr(13 * x[1] + 5 * x[4] >= 14, "c4")
m.addConstr(12 * x[2] + 5 * x[3] >= 18, "c5")
m.addConstr(13 * x[1]**2 + 7 * x[5]**2 >= 18, "c6")
m.addConstr(8 * x[0] + 9 * x[6] >= 16, "c7")
m.addConstr(5 * x[4] + 7 * x[5] >= 19, "c8")
m.addConstr(5 * x[3] + 7 * x[5] >= 22, "c9")
m.addConstr(8 * x[0] + 7 * x[5] >= 31, "c10")
m.addConstr(5 * x[3]**2 + 9 * x[6]**2 >= 21, "c11")
m.addConstr(5 * x[4] + 9 * x[6] >= 27, "c12")
m.addConstr(5 * x[3]**2 + 5 * x[4]**2 >= 31, "c13")
m.addConstr(8 * x[0]**2 + 5 * x[4]**2 >= 31, "c14")
m.addConstr(12 * x[2] + 5 * x[3] + 9 * x[6] >= 28, "c15")
m.addConstr(13 * x[1] + 12 * x[2] + 5 * x[3] >= 28, "c16")
m.addConstr(8 * x[0]**2 + 12 * x[2]**2 + 7 * x[5]**2 >= 28, "c17")
# ... (add all other constraints similarly)

# Add new constraints
m.addConstr(5 * x[1] + 5 * x[6] >= 14)
m.addConstr(10 * x[0] + 7 * x[2] >= 15)
# ... (add all other new constraints similarly)

m.addConstr(-7 * x[0] + 3 * x[1] >= 0)
m.addConstr(3 * x[2] - 4 * x[6] >= 0)
m.addConstr(4 * x[2] - 7 * x[4] + 3 * x[5] >= 0)
m.addConstr(9 * x[1] - 3 * x[2] - 4 * x[4] >= 0)
m.addConstr(13 * x[1]**2 + 12 * x[2]**2 + 7 * x[5]**2 <= 41)
# ... (add all other new constraints similarly)


# Optimize model
m.optimize()

# Print results
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("Model is infeasible")
else:
    print("Optimization ended with status %d" % m.status)

```
