## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'oranges', 'kiwis', and 'bananas', which we will denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $7.89x_0 + 1.44x_1 + 4.36x_2$.

## 3: Define the symbolic variables and their corresponding natural language objects
The symbolic variables and their corresponding objects are:
- $x_0$ : oranges
- $x_1$ : kiwis
- $x_2$ : bananas

## 4: List the constraints in symbolic notation
The constraints are:
1. $6x_0 + 6x_1 \geq 34$ (at least 34 grams of fat from oranges and kiwis)
2. $6x_1 + 6x_2 \geq 17$ (at least 17 grams of fat from kiwis and bananas)
3. $6x_0 + 6x_2 \geq 31$ (at least 31 grams of fat from oranges and bananas)
4. $5x_0 - 10x_1 \geq 0$ (5 times oranges minus 10 times kiwis is non-negative)
5. $6x_0 + 6x_2 \leq 98$ (no more than 98 grams of fat from oranges and bananas)
6. $6x_0 + 6x_1 \leq 62$ (no more than 62 grams of fat from oranges and kiwis)
7. $6x_1 + 6x_2 \leq 92$ (no more than 92 grams of fat from kiwis and bananas)
8. $6x_0 + 6x_1 + 6x_2 \leq 49$ (no more than 49 grams of fat from all)
9. $6x_0 + 6x_1 + 6x_2 \leq 49$ (same as above, redundant)
10. $12x_1 + 8x_2 \leq 90$ (no more than $90 on kiwis and bananas)
11. $9x_0 + 12x_1 \leq 85$ (no more than $85 on oranges and kiwis)
12. $9x_0 + 12x_1 + 8x_2 \leq 85$ (no more than $85 on all)

## 5: Consider the variable constraints
- $x_0, x_1, x_2$ are integers.

## 6: Write down the problem in a structured format
```json
{
    'sym_variables': [('x0', 'oranges'), ('x1', 'kiwis'), ('x2', 'bananas')],
    'objective_function': '7.89*x0 + 1.44*x1 + 4.36*x2',
    'constraints': [
        '6*x0 + 6*x1 >= 34',
        '6*x1 + 6*x2 >= 17',
        '6*x0 + 6*x2 >= 31',
        '5*x0 - 10*x1 >= 0',
        '6*x0 + 6*x2 <= 98',
        '6*x0 + 6*x1 <= 62',
        '6*x1 + 6*x2 <= 92',
        '6*x0 + 6*x1 + 6*x2 <= 49',
        '12*x1 + 8*x2 <= 90',
        '9*x0 + 12*x1 <= 85',
        '9*x0 + 12*x1 + 8*x2 <= 85'
    ]
}
```

## 7: Implement the problem using Gurobi
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="oranges", vtype=gurobi.GRB.INTEGER)
x1 = m.addVar(name="kiwis", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="bananas", vtype=gurobi.GRB.INTEGER)

# Define the objective function
m.setObjective(7.89*x0 + 1.44*x1 + 4.36*x2, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(6*x0 + 6*x1 >= 34)
m.addConstr(6*x1 + 6*x2 >= 17)
m.addConstr(6*x0 + 6*x2 >= 31)
m.addConstr(5*x0 - 10*x1 >= 0)
m.addConstr(6*x0 + 6*x2 <= 98)
m.addConstr(6*x0 + 6*x1 <= 62)
m.addConstr(6*x1 + 6*x2 <= 92)
m.addConstr(6*x0 + 6*x1 + 6*x2 <= 49)
m.addConstr(12*x1 + 8*x2 <= 90)
m.addConstr(9*x0 + 12*x1 <= 85)
m.addConstr(9*x0 + 12*x1 + 8*x2 <= 85)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Oranges: ", x0.varValue)
    print("Kiwis: ", x1.varValue)
    print("Bananas: ", x2.varValue)
else:
    print("The model is infeasible")
```