## Step 1: Define the symbolic representation of the variables
The variables are 'blueberry pies', 'bananas', and 'cornichons', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : blueberry pies
- $x_1$ : bananas
- $x_2$ : cornichons

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is $1x_0 + 5x_1 + 9x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $13x_1 + 4x_2 \geq 63$
- $7x_0 + 4x_2 \geq 39$
- $7x_0 + 13x_1 \geq 61$
- $7x_0 + 13x_1 + 4x_2 \geq 61$
- $15x_0 + 20x_1 \geq 69$
- $20x_1 + 19x_2 \geq 93$
- $15x_0 + 20x_1 + 19x_2 \geq 93$
- $-9x_1 + 2x_2 \geq 0$
- $7x_0 + 13x_1 \leq 88$
- $13x_1 + 4x_2 \leq 167$
- $15x_0 + 19x_2 \leq 143$
- $15x_0 + 20x_1 \leq 229$
- $20x_1 + 19x_2 \leq 289$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'blueberry pies'), 
        ('x1', 'bananas'), 
        ('x2', 'cornichons')
    ], 
    'objective_function': '1*x0 + 5*x1 + 9*x2', 
    'constraints': [
        '13*x1 + 4*x2 >= 63', 
        '7*x0 + 4*x2 >= 39', 
        '7*x0 + 13*x1 >= 61', 
        '7*x0 + 13*x1 + 4*x2 >= 61', 
        '15*x0 + 20*x1 >= 69', 
        '20*x1 + 19*x2 >= 93', 
        '15*x0 + 20*x1 + 19*x2 >= 93', 
        '-9*x1 + 2*x2 >= 0', 
        '7*x0 + 13*x1 <= 88', 
        '13*x1 + 4*x2 <= 167', 
        '15*x0 + 19*x2 <= 143', 
        '15*x0 + 20*x1 <= 229', 
        '20*x1 + 19*x2 <= 289'
    ]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="blueberry_pies", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x1 = model.addVar(name="bananas", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="cornichons", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(1 * x0 + 5 * x1 + 9 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(13 * x1 + 4 * x2 >= 63)
    model.addConstr(7 * x0 + 4 * x2 >= 39)
    model.addConstr(7 * x0 + 13 * x1 >= 61)
    model.addConstr(7 * x0 + 13 * x1 + 4 * x2 >= 61)
    model.addConstr(15 * x0 + 20 * x1 >= 69)
    model.addConstr(20 * x1 + 19 * x2 >= 93)
    model.addConstr(15 * x0 + 20 * x1 + 19 * x2 >= 93)
    model.addConstr(-9 * x1 + 2 * x2 >= 0)
    model.addConstr(7 * x0 + 13 * x1 <= 88)
    model.addConstr(13 * x1 + 4 * x2 <= 167)
    model.addConstr(15 * x0 + 19 * x2 <= 143)
    model.addConstr(15 * x0 + 20 * x1 <= 229)
    model.addConstr(20 * x1 + 19 * x2 <= 289)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Blueberry pies: {x0.varValue}")
        print(f"Bananas: {x1.varValue}")
        print(f"Cornichons: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```