To solve the given optimization problem using Gurobi, we first need to translate the natural language description into a mathematical formulation. The objective is to minimize the function:

1 * (amount of kiwis) + 5 * (amount of cantaloupes) + 7 * (amount of cherry pies) + 7 * (amount of cornichons)

Subject to several constraints related to the grams of carbohydrates in each item and additional linear constraints.

Let's denote:
- \(x_0\) as the amount of kiwis,
- \(x_1\) as the amount of cantaloupes,
- \(x_2\) as the amount of cherry pies,
- \(x_3\) as the amount of cornichons.

Given data:
- Each kiwi has 5 grams of carbohydrates.
- Each cantaloupe has 20 grams of carbohydrates.
- Each cherry pie has 2 grams of carbohydrates.
- Each cornichon has 9 grams of carbohydrates.

Constraints:
1. \(5x_0 + 9x_3 \geq 59\)
2. \(20x_1 + 2x_2 \geq 42\)
3. \(2x_2 + 9x_3 \geq 55\)
4. \(5x_0 + 2x_2 \geq 39\)
5. \(5x_0 + 20x_1 + 2x_2 + 9x_3 \geq 39\)
6. \(6x_2 - 3x_3 \geq 0\)
7. \(-9x_1 + 2x_2 \geq 0\)

Non-negativity and integrality constraints:
- \(x_0\) is a non-negative integer (whole number of kiwis).
- \(x_1\) and \(x_2\) are non-negative integers (whole numbers of cantaloupes and cherry pies, respectively).
- \(x_3\) is a non-negative float (can be any non-negative real number).

Here's how we can implement this in Gurobi:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="kiwis")
x1 = m.addVar(vtype=GRB.INTEGER, name="cantaloupes")
x2 = m.addVar(vtype=GRB.INTEGER, name="cherry_pies")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="cornichons")

# Objective function
m.setObjective(1*x0 + 5*x1 + 7*x2 + 7*x3, GRB.MINIMIZE)

# Constraints
m.addConstr(5*x0 + 9*x3 >= 59, "carb_kiwis_cornichons")
m.addConstr(20*x1 + 2*x2 >= 42, "carb_cantaloupes_pies")
m.addConstr(2*x2 + 9*x3 >= 55, "carb_pies_cornichons")
m.addConstr(5*x0 + 2*x2 >= 39, "carb_kiwis_pies")
m.addConstr(5*x0 + 20*x1 + 2*x2 + 9*x3 >= 39, "total_carb")
m.addConstr(6*x2 - 3*x3 >= 0, "pies_cornichons_ratio")
m.addConstr(-9*x1 + 2*x2 >= 0, "cantaloupes_pies_ratio")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Kiwis: {x0.x}")
    print(f"Cantaloupes: {x1.x}")
    print(f"Cherry Pies: {x2.x}")
    print(f"Cornichons: {x3.x}")
else:
    print("No optimal solution found")
```