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

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $3.73x_0 + 5.92x_1 + 2.9x_2$.

## Step 3: Define the resources/attributes for each variable
The resources/attributes are given as:
- $r_0$: sourness index with $x_0 = 8$, $x_1 = 3$, $x_2 = 1$ and upper bound $93$
- $r_1$: grams of fat with $x_0 = 7$, $x_1 = 4$, $x_2 = 5$ and upper bound $103$
- $r_2$: umami index with $x_0 = 7$, $x_1 = 5$, $x_2 = 7$ and upper bound $108$

## 4: Convert the constraints into a symbolic representation
The constraints are:
- $8x_0 + 3x_1 \geq 22$
- $3x_1 + x_2 \geq 12$
- $8x_0 + 3x_1 + x_2 \geq 12$
- $7x_0 + 4x_1 \geq 15$
- $7x_0 + 4x_1 + 5x_2 \geq 15$
- $7x_0 + 7x_2 \geq 15$
- $7x_0 + 5x_1 \geq 33$
- $7x_0 + 5x_1 + 7x_2 \geq 33$
- $2x_0 - 2x_1 \geq 0$
- $x_1 - 9x_2 \geq 0$
- $7x_0 + 4x_1 \leq 84$

## 5: Create the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'lemons'), ('x1', 'strips of bacon'), ('x2', 'blueberry pies')],
    'objective_function': '3.73*x0 + 5.92*x1 + 2.9*x2',
    'constraints': [
        '8*x0 + 3*x1 >= 22',
        '3*x1 + x2 >= 12',
        '8*x0 + 3*x1 + x2 >= 12',
        '7*x0 + 4*x1 >= 15',
        '7*x0 + 4*x1 + 5*x2 >= 15',
        '7*x0 + 7*x2 >= 15',
        '7*x0 + 5*x1 >= 33',
        '7*x0 + 5*x1 + 7*x2 >= 33',
        '2*x0 - 2*x1 >= 0',
        'x1 - 9*x2 >= 0',
        '7*x0 + 4*x1 <= 84'
    ]
}
```

## 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="lemons", lb=0)
    x1 = model.addVar(name="strips of bacon", lb=0)
    x2 = model.addVar(name="blueberry pies", lb=0)

    # Define the objective function
    model.setObjective(3.73*x0 + 5.92*x1 + 2.9*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(8*x0 + 3*x1 >= 22, name="sourness_lemons_bacon")
    model.addConstr(3*x1 + x2 >= 12, name="sourness_bacon_pies")
    model.addConstr(8*x0 + 3*x1 + x2 >= 12, name="sourness_total")
    model.addConstr(7*x0 + 4*x1 >= 15, name="fat_lemons_bacon")
    model.addConstr(7*x0 + 4*x1 + 5*x2 >= 15, name="fat_total")
    model.addConstr(7*x0 + 7*x2 >= 15, name="umami_lemons_pies")
    model.addConstr(7*x0 + 5*x1 >= 33, name="umami_lemons_bacon")
    model.addConstr(7*x0 + 5*x1 + 7*x2 >= 33, name="umami_total")
    model.addConstr(2*x0 - 2*x1 >= 0, name="lemons_bacon_ratio")
    model.addConstr(x1 - 9*x2 >= 0, name="bacon_pies_ratio")
    model.addConstr(7*x0 + 4*x1 <= 84, name="fat_lemons_bacon_limit")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Lemons: ", x0.varValue)
        print("Strips of bacon: ", x1.varValue)
        print("Blueberry pies: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```