## Step 1: Define the symbolic representation of the variables
The variables are ['cornichons', 'strips of bacon', 'bananas'] which can be represented symbolically as ['x1', 'x2', 'x3'].

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- x1: cornichons
- x2: strips of bacon
- x3: bananas

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is: $4.4x_1 + 9.0x_2 + 8.18x_3$

## 4: List the constraints in symbolic notation
The constraints are:
- $11x_1 \leq 147$ (tastiness rating of cornichons, but this seems to be an upper bound on x1, not a constraint involving other variables)
- $8x_2 \leq 147$ (tastiness rating of strips of bacon, but similarly, this seems to be an upper bound on x2)
- $3x_3 \leq 147$ (tastiness rating of bananas, an upper bound on x3)
- $8x_2 + 3x_3 \geq 41$ (total combined tastiness rating from strips of bacon and bananas)
- $11x_1 + 8x_2 \geq 18$ (total combined tastiness rating from cornichons and strips of bacon)
- $11x_1 + 8x_2 + 3x_3 \geq 18$ (total combined tastiness rating from all)
- $2x_1 - 8x_2 \geq 0$
- $-x_2 + 7x_3 \geq 0$

## 5: Correct and refine the constraints based on the problem description
From the problem, we understand that $x_1, x_2, x_3$ can be fractional and there are upper bounds for tastiness ratings:
- The tastiness rating of cornichons is given as 11, which implies $11x_1 \leq 147$ or $x_1 \leq \frac{147}{11}$, but we focus on constraints involving multiple variables.
- The upper bounds for $x_1, x_2, x_3$ based on tastiness ratings are:
  - $x_1 \leq \frac{147}{11} = 13.3636...$
  - $x_2 \leq \frac{147}{8} = 18.375$
  - $x_3 \leq \frac{147}{3} = 49$

## 6: Formulate the optimization problem in Gurobi
We will use Gurobi to solve this linear programming problem.

## 7: Write the Gurobi code
```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="cornichons", lb=0, ub=147/11, vtype=gurobi.GRB.CONTINUOUS)
x2 = model.addVar(name="strips of bacon", lb=0, ub=147/8, vtype=gurobi.GRB.CONTINUOUS)
x3 = model.addVar(name="bananas", lb=0, ub=147/3, vtype=gurobi.GRB.CONTINUOUS)

# Define the objective function
model.setObjective(4.4 * x1 + 9.0 * x2 + 8.18 * x3, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(8 * x2 + 3 * x3 >= 41, name="tastiness_bacon_bananas")
model.addConstr(11 * x1 + 8 * x2 >= 18, name="tastiness_cornichons_bacon")
model.addConstr(11 * x1 + 8 * x2 + 3 * x3 >= 18, name="tastiness_all")
model.addConstr(2 * x1 - 8 * x2 >= 0, name="cornichons_bacon_ratio")
model.addConstr(-x2 + 7 * x3 >= 0, name="bacon_bananas_ratio")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("cornichons: ", x1.varValue)
    print("strips of bacon: ", x2.varValue)
    print("bananas: ", x3.varValue)
else:
    print("The model is infeasible")
```

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