## Step 1: Define the symbolic representation of the variables
The variables are 'strips of bacon', 'corn cobs', and 'bananas', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $8.35x_1 + 6.13x_2 + 3.25x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
1. $8x_1 + 4x_2 + 2x_3 \leq 65$ (total fat constraint)
2. $4x_2 + 2x_3 \geq 21$ (fat from corn cobs and bananas)
3. $8x_1 + 4x_2 \geq 9$ (fat from strips of bacon and corn cobs)
4. $8x_1 + 2x_3 \geq 11$ (fat from strips of bacon and bananas)
5. $8x_1 + 4x_2 + 2x_3 \geq 11$ (fat from all sources)
6. $10x_1 - 4x_2 \geq 0$ (constraint on strips of bacon and corn cobs)
7. $-6x_2 + 3x_3 \geq 0$ (constraint on corn cobs and bananas)
8. $-x_1 + 9x_3 \geq 0$ (constraint on strips of bacon and bananas)

## 4: Create the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'strips of bacon'), ('x2', 'corn cobs'), ('x3', 'bananas')],
'objective_function': '8.35*x1 + 6.13*x2 + 3.25*x3',
'constraints': [
'8*x1 + 4*x2 + 2*x3 <= 65',
'4*x2 + 2*x3 >= 21',
'8*x1 + 4*x2 >= 9',
'8*x1 + 2*x3 >= 11',
'8*x1 + 4*x2 + 2*x3 >= 11',
'10*x1 - 4*x2 >= 0',
'-6*x2 + 3*x3 >= 0',
'-x1 + 9*x3 >= 0'
]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="strips_of_bacon", lb=0)  # strips of bacon
x2 = model.addVar(name="corn_cobs", lb=0)  # corn cobs
x3 = model.addVar(name="bananas", lb=0)  # bananas

# Objective function
model.setObjective(8.35*x1 + 6.13*x2 + 3.25*x3, gurobi.GRB.MINIMIZE)

# Constraints
model.addConstr(8*x1 + 4*x2 + 2*x3 <= 65)  # total fat constraint
model.addConstr(4*x2 + 2*x3 >= 21)  # fat from corn cobs and bananas
model.addConstr(8*x1 + 4*x2 >= 9)  # fat from strips of bacon and corn cobs
model.addConstr(8*x1 + 2*x3 >= 11)  # fat from strips of bacon and bananas
model.addConstr(8*x1 + 4*x2 + 2*x3 >= 11)  # fat from all sources
model.addConstr(10*x1 - 4*x2 >= 0)  # constraint on strips of bacon and corn cobs
model.addConstr(-6*x2 + 3*x3 >= 0)  # constraint on corn cobs and bananas
model.addConstr(-x1 + 9*x3 >= 0)  # constraint on strips of bacon and bananas

# Optimize the model
model.optimize()

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