## Problem Description and Formulation

The problem is an optimization problem with a linear objective function and linear constraints. The goal is to minimize the objective function subject to the given constraints.

### Variables and Resources

The variables are:
- `kiwis`
- `cantaloupes`
- `cherry pies`
- `cornichons`

The resources/attributes are given as:
```markdown
{'r0': {'description': 'grams of carbohydrates', 'upper_bound': 261, 'x0': 5, 'x1': 20, 'x2': 2, 'x3': 9}}
```
This implies that each variable has a carbohydrate contribution: 
- `kiwis`: 5 grams
- `cantaloupes`: 20 grams
- `cherry pies`: 2 grams
- `cornichons`: 9 grams

### Objective Function

The objective function to minimize is:
\[ 1 \times \text{kiwis} + 5 \times \text{cantaloupes} + 7 \times \text{cherry pies} + 7 \times \text{cornichons} \]

### Constraints

1. Carbohydrate content of each item is given.
2. `kiwis + cornichons >= 59 / 1` (at least 59 grams of carbohydrates from kiwis and cornichons)
3. `cantaloupes + cherry pies >= 42 / 1` (at least 42 grams of carbohydrates from cantaloupes and cherry pies)
4. `cherry pies + cornichons >= 55 / 1` (at least 55 grams of carbohydrates from cherry pies and cornichons)
5. `kiwis + cherry pies >= 39 / 1` (at least 39 grams of carbohydrates from kiwis and cherry pies)
6. `kiwis + cantaloupes + cherry pies + cornichons >= 39 / 1` (at least 39 grams of carbohydrates from all)
7. `6 * cherry pies - 3 * cornichons >= 0`
8. `-9 * cantaloupes + 2 * cherry pies >= 0`

### Variable Constraints

- `kiwis` is non-fractional (integer)
- `cantaloupes` is a whole number (integer)
- `cherry pies` is a whole number (integer)
- `cornichons` can be a float

## Gurobi Code Formulation

```python
import gurobi

def optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define variables
    kiwis = model.addVar(name="kiwis", vtype=gurobi.GRB.INTEGER)
    cantaloupes = model.addVar(name="cantaloupes", vtype=gurobi.GRB.INTEGER)
    cherry_pies = model.addVar(name="cherry_pies", vtype=gurobi.GRB.INTEGER)
    cornichons = model.addVar(name="cornichons")

    # Objective function
    model.setObjective(kiwis + 5 * cantaloupes + 7 * cherry_pies + 7 * cornichons, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(kiwis + cornichons >= 59, name="carbohydrates_kiwis_cornichons")
    model.addConstr(cantaloupes + cherry_pies >= 42, name="carbohydrates_cantaloupes_cherry_pies")
    model.addConstr(cherry_pies + cornichons >= 55, name="carbohydrates_cherry_pies_cornichons")
    model.addConstr(kiwis + cherry_pies >= 39, name="carbohydrates_kiwis_cherry_pies")
    model.addConstr(kiwis + cantaloupes + cherry_pies + cornichons >= 39, name="carbohydrates_all")
    model.addConstr(6 * cherry_pies - 3 * cornichons >= 0, name="cherry_pies_cornichons")
    model.addConstr(-9 * cantaloupes + 2 * cherry_pies >= 0, name="cantaloupes_cherry_pies")

    # Set bounds for carbohydrates
    model.addConstr(5 * kiwis + 20 * cantaloupes + 2 * cherry_pies + 9 * cornichons <= 261, name="total_carbohydrates")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Kiwis: ", kiwis.varValue)
        print("Cantaloupes: ", cantaloupes.varValue)
        print("Cherry Pies: ", cherry_pies.varValue)
        print("Cornichons: ", cornichons.varValue)
    else:
        print("The model is infeasible")

optimization_problem()
```