To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (cornichons, strips of bacon, bananas), formulating the objective function using these variables, and then listing all constraints in terms of these variables.

### Symbolic Representation

Let's define the variables as follows:
- \(x_1\) represents the number of cornichons,
- \(x_2\) represents the number of strips of bacon,
- \(x_3\) represents the number of bananas.

The objective function to minimize is: \(4.4x_1 + 9.0x_2 + 8.18x_3\).

The constraints based on the tastiness ratings and other conditions are:
1. \(11x_1 + 8x_2 \geq 18\),
2. \(11x_1 + 8x_2 + 3x_3 \geq 18\),
3. \(8x_2 + 3x_3 \geq 41\),
4. \(2x_1 - 8x_2 \geq 0\),
5. \(-x_2 + 7x_3 \geq 0\).

All variables (\(x_1, x_2, x_3\)) can be non-integer.

### Symbolic Representation in JSON Format

```json
{
  'sym_variables': [('x1', 'cornichons'), ('x2', 'strips of bacon'), ('x3', 'bananas')],
  'objective_function': '4.4*x1 + 9.0*x2 + 8.18*x3',
  'constraints': [
    '11*x1 + 8*x2 >= 18',
    '11*x1 + 8*x2 + 3*x3 >= 18',
    '8*x2 + 3*x3 >= 41',
    '2*x1 - 8*x2 >= 0',
    '-x2 + 7*x3 >= 0'
  ]
}
```

### Gurobi Code

Now, let's implement this optimization problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="cornichons")  # Can be fractional
x2 = m.addVar(lb=0, name="strips_of_bacon")  # Can be non-integer
x3 = m.addVar(lb=0, name="bananas")  # Can be non-integer

# Set the objective function
m.setObjective(4.4*x1 + 9.0*x2 + 8.18*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(11*x1 + 8*x2 >= 18, name="tastiness_cornichons_bacon")
m.addConstr(11*x1 + 8*x2 + 3*x3 >= 18, name="total_tastiness_min_18")
m.addConstr(8*x2 + 3*x3 >= 41, name="bacon_bananas_tastiness")
m.addConstr(2*x1 - 8*x2 >= 0, name="cornichons_vs_bacon")
m.addConstr(-x2 + 7*x3 >= 0, name="bacon_vs_bananas")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Cornichons: {x1.x}")
    print(f"Strips of Bacon: {x2.x}")
    print(f"Bananas: {x3.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```