## Problem Description and Gurobi Code

The problem is a linear optimization problem where we need to maximize an objective function subject to several constraints. The objective function is:

Maximize: 8 * oranges + 6 * bananas + 5 * peanutbutter sandwiches + 3 * pickles + 6 * cornichons

Subject to constraints on calcium intake from various food items.

### Gurobi Code

```python
import gurobi

# Create a new Gurobi model
m = gurobi.Model()

# Define the variables
oranges = m.addVar(name="oranges", lb=0)
bananas = m.addVar(name="bananas", lb=0)
peanutbutter_sandwiches = m.addVar(name="peanutbutter_sandwiches", lb=0)
pickles = m.addVar(name="pickles", lb=0)
cornichons = m.addVar(name="cornichons", lb=0)

# Objective function
m.setObjective(8 * oranges + 6 * bananas + 5 * peanutbutter_sandwiches + 3 * pickles + 6 * cornichons, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(11 * oranges + 9 * bananas + 2 * peanutbutter_sandwiches + 17 * pickles + 14 * cornichons <= 215, name="total_calcium")

# Constraints for calcium intake from various food items
m.addConstr(2 * peanutbutter_sandwiches + 14 * cornichons >= 17, name="peanutbutter_sandwiches_cornichons_calcium")
m.addConstr(11 * oranges + 14 * cornichons >= 16, name="oranges_cornichons_calcium")
m.addConstr(9 * bananas + 2 * peanutbutter_sandwiches >= 18, name="bananas_peanutbutter_sandwiches_calcium")
m.addConstr(2 * peanutbutter_sandwiches + 17 * pickles >= 30, name="peanutbutter_sandwiches_pickles_calcium")
m.addConstr(2 * peanutbutter_sandwiches + 17 * pickles + 14 * cornichons >= 24, name="peanutbutter_sandwiches_pickles_cornichons_calcium")
m.addConstr(11 * oranges + 17 * pickles + 14 * cornichons >= 24, name="oranges_pickles_cornichons_calcium")
m.addConstr(9 * bananas + 2 * peanutbutter_sandwiches + 14 * cornichons >= 24, name="bananas_peanutbutter_sandwiches_cornichons_calcium")
m.addConstr(9 * bananas + 2 * peanutbutter_sandwiches + 17 * pickles >= 24, name="bananas_peanutbutter_sandwiches_pickles_calcium")
m.addConstr(2 * peanutbutter_sandwiches + 17 * pickles + 14 * cornichons >= 27, name="peanutbutter_sandwiches_pickles_cornichons_calcium_2")
m.addConstr(11 * oranges + 17 * pickles + 14 * cornichons >= 27, name="oranges_pickles_cornichons_calcium_2")
m.addConstr(9 * bananas + 2 * peanutbutter_sandwiches + 14 * cornichons >= 27, name="bananas_peanutbutter_sandwiches_cornichons_calcium_2")
m.addConstr(9 * bananas + 2 * peanutbutter_sandwiches + 17 * pickles >= 27, name="bananas_peanutbutter_sandwiches_pickles_calcium_2")
m.addConstr(2 * peanutbutter_sandwiches + 17 * pickles + 14 * cornichons >= 35, name="peanutbutter_sandwiches_pickles_cornichons_calcium_3")
m.addConstr(11 * oranges + 17 * pickles + 14 * cornichons >= 35, name="oranges_pickles_cornichons_calcium_3")
m.addConstr(9 * bananas + 2 * peanutbutter_sandwiches + 14 * cornichons >= 35, name="bananas_peanutbutter_sandwiches_cornichons_calcium_3")
m.addConstr(9 * bananas + 2 * peanutbutter_sandwiches + 17 * pickles >= 35, name="bananas_peanutbutter_sandwiches_pickles_calcium_3")

# Upper bound constraints
m.addConstr(11 * oranges + 14 * cornichons <= 55, name="oranges_cornichons_calcium_ub")
m.addConstr(2 * peanutbutter_sandwiches + 14 * cornichons <= 158, name="peanutbutter_sandwiches_cornichons_calcium_ub")
m.addConstr(11 * oranges + 17 * pickles <= 154, name="oranges_pickles_calcium_ub")
m.addConstr(17 * pickles + 14 * cornichons <= 122, name="pickles_cornichons_calcium_ub")
m.addConstr(11 * oranges + 2 * peanutbutter_sandwiches <= 204, name="oranges_peanutbutter_sandwiches_calcium_ub")
m.addConstr(2 * peanutbutter_sandwiches + 17 * pickles <= 119, name="peanutbutter_sandwiches_pickles_calcium_ub")
m.addConstr(11 * oranges + 2 * peanutbutter_sandwiches + 17 * pickles <= 85, name="oranges_peanutbutter_sandwiches_pickles_calcium_ub")
m.addConstr(11 * oranges + 9 * bananas + 2 * peanutbutter_sandwiches + 17 * pickles + 14 * cornichons <= 85, name="total_calcium_ub")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Oranges: ", oranges.varValue)
    print("Bananas: ", bananas.varValue)
    print("Peanutbutter sandwiches: ", peanutbutter_sandwiches.varValue)
    print("Pickles: ", pickles.varValue)
    print("Cornichons: ", cornichons.varValue)
else:
    print("The model is infeasible.")
```