```json
{
  "sym_variables": [
    ("x0", "lemons"),
    ("x1", "ham sandwiches"),
    ("x2", "cherry pies"),
    ("x3", "fruit salads")
  ],
  "objective_function": "7.26*x0 + 6.62*x1 + 1.42*x2 + 2.56*x3",
  "constraints": [
    "5.8*x0 + 8.4*x3 >= 27",
    "2.9*x2 + 8.4*x3 >= 37",
    "3.85*x1 + 8.4*x3 >= 30",
    "5.8*x0 + 3.85*x1 + 2.9*x2 >= 31",
    "5.8*x0 + 2.9*x2 + 8.4*x3 >= 31",
    "5.8*x0 + 3.85*x1 + 2.9*x2 >= 41",
    "5.8*x0 + 2.9*x2 + 8.4*x3 >= 41",
    "5.8*x0 + 3.85*x1 + 2.9*x2 + 8.4*x3 >= 41",
    "5.15*x0 + 7.62*x3 >= 27",
    "5.15*x0 + 3.63*x1 >= 24",
    "3.3*x2 + 7.62*x3 >= 47",
    "3.63*x1 + 3.3*x2 >= 38",
    "5.15*x0 + 3.3*x2 + 7.62*x3 >= 32",
    "5.15*x0 + 3.63*x1 + 3.3*x2 + 7.62*x3 >= 32",
    "-6*x2 + 7*x3 >= 0",
    "2*x1 - x2 >= 0",
    "5.8*x0 + 2.9*x2 <= 130",
    "2.9*x2 + 8.4*x3 <= 169",
    "3.85*x1 + 2.9*x2 <= 181",
    "3.85*x1 + 8.4*x3 <= 73",
    "5.15*x0 + 3.3*x2 + 7.62*x3 <= 68",
    "5.8*x0 + 3.85*x1 + 2.9*x2 + 8.4*x3 <= 207", 
    "5.15*x0 + 3.63*x1 + 3.3*x2 + 7.62*x3 <= 206"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
lemons = m.addVar(lb=0, name="lemons")
ham_sandwiches = m.addVar(lb=0, name="ham_sandwiches")
cherry_pies = m.addVar(lb=0, name="cherry_pies")
fruit_salads = m.addVar(lb=0, name="fruit_salads")


# Set objective function
m.setObjective(7.26 * lemons + 6.62 * ham_sandwiches + 1.42 * cherry_pies + 2.56 * fruit_salads, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(5.8 * lemons + 8.4 * fruit_salads >= 27)
m.addConstr(2.9 * cherry_pies + 8.4 * fruit_salads >= 37)
m.addConstr(3.85 * ham_sandwiches + 8.4 * fruit_salads >= 30)
m.addConstr(5.8 * lemons + 3.85 * ham_sandwiches + 2.9 * cherry_pies >= 31)
m.addConstr(5.8 * lemons + 2.9 * cherry_pies + 8.4 * fruit_salads >= 31)
m.addConstr(5.8 * lemons + 3.85 * ham_sandwiches + 2.9 * cherry_pies >= 41)
m.addConstr(5.8 * lemons + 2.9 * cherry_pies + 8.4 * fruit_salads >= 41)
m.addConstr(5.8 * lemons + 3.85 * ham_sandwiches + 2.9 * cherry_pies + 8.4 * fruit_salads >= 41)
m.addConstr(5.15 * lemons + 7.62 * fruit_salads >= 27)
m.addConstr(5.15 * lemons + 3.63 * ham_sandwiches >= 24)
m.addConstr(3.3 * cherry_pies + 7.62 * fruit_salads >= 47)
m.addConstr(3.63 * ham_sandwiches + 3.3 * cherry_pies >= 38)
m.addConstr(5.15 * lemons + 3.3 * cherry_pies + 7.62 * fruit_salads >= 32)
m.addConstr(5.15 * lemons + 3.63 * ham_sandwiches + 3.3 * cherry_pies + 7.62 * fruit_salads >= 32)
m.addConstr(-6 * cherry_pies + 7 * fruit_salads >= 0)
m.addConstr(2 * ham_sandwiches - cherry_pies >= 0)
m.addConstr(5.8 * lemons + 2.9 * cherry_pies <= 130)
m.addConstr(2.9 * cherry_pies + 8.4 * fruit_salads <= 169)
m.addConstr(3.85 * ham_sandwiches + 2.9 * cherry_pies <= 181)
m.addConstr(3.85 * ham_sandwiches + 8.4 * fruit_salads <= 73)
m.addConstr(5.15 * lemons + 3.3 * cherry_pies + 7.62 * fruit_salads <= 68)

m.addConstr(5.8 * lemons + 3.85 * ham_sandwiches + 2.9 * cherry_pies + 8.4 * fruit_salads <= 207)
m.addConstr(5.15 * lemons + 3.63 * ham_sandwiches + 3.3 * cherry_pies + 7.62 * fruit_salads <= 206)


# 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("The problem is infeasible.")
else:
    print("The problem could not be solved to optimality.")

```