```json
{
  "sym_variables": [
    ("x0", "oranges"),
    ("x1", "milkshakes"),
    ("x2", "pickles")
  ],
  "objective_function": "2.69*x0 + 1.69*x1 + 1.85*x2",
  "constraints": [
    "2*x0 + 10*x2 >= 33",
    "2*x0 + 9*x1 >= 36",
    "2*x0 + 9*x1 + 10*x2 >= 36",
    "11*x0 + 13*x2 >= 31",
    "11*x0 + 16*x1 >= 25",
    "11*x0 + 16*x1 + 13*x2 >= 25",
    "-8*x0 + 4*x1 >= 0",
    "2*x1 - 9*x2 >= 0",
    "9*x1 + 10*x2 <= 91",
    "16*x1 + 13*x2 <= 97",
    "2*x0 + 9*x1 + 10*x2 <= 127",
    "11*x0 + 16*x1 + 13*x2 <= 129",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
oranges = m.addVar(vtype=gp.GRB.CONTINUOUS, name="oranges")
milkshakes = m.addVar(vtype=gp.GRB.CONTINUOUS, name="milkshakes")
pickles = m.addVar(vtype=gp.GRB.CONTINUOUS, name="pickles")

# Set objective function
m.setObjective(2.69 * oranges + 1.69 * milkshakes + 1.85 * pickles, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(2 * oranges + 10 * pickles >= 33, "c1")
m.addConstr(2 * oranges + 9 * milkshakes >= 36, "c2")
m.addConstr(2 * oranges + 9 * milkshakes + 10 * pickles >= 36, "c3")
m.addConstr(11 * oranges + 13 * pickles >= 31, "c4")
m.addConstr(11 * oranges + 16 * milkshakes >= 25, "c5")
m.addConstr(11 * oranges + 16 * milkshakes + 13 * pickles >= 25, "c6")
m.addConstr(-8 * oranges + 4 * milkshakes >= 0, "c7")
m.addConstr(2 * milkshakes - 9 * pickles >= 0, "c8")
m.addConstr(9 * milkshakes + 10 * pickles <= 91, "c9")
m.addConstr(16 * milkshakes + 13 * pickles <= 97, "c10")

# Resource Constraints
m.addConstr(2 * oranges + 9 * milkshakes + 10 * pickles <= 127, "fat_ub")
m.addConstr(11 * oranges + 16 * milkshakes + 13 * pickles <= 129, "carb_ub")


# Non-negativity constraints are added implicitly by setting vtype to CONTINUOUS

# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('oranges:', oranges.x)
    print('milkshakes:', milkshakes.x)
    print('pickles:', pickles.x)
elif m.status == gp.GRB.INFEASIBLE:
    print('The model is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```