## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'kiwis' and 'strawberries', which we can represent symbolically as $x_1$ and $x_2$ respectively. The objective function to minimize is $1x_1^2 + 3x_1x_2 + 7x_2^2 + 9x_1 + 3x_2$. The constraints are:
- $1.97x_1 + 4.63x_2 \geq 71$
- $1.97x_1 + 4.63x_2 \leq 138$
- $8x_1^2 - 8x_2^2 \geq 0$
- $x_1$ is a continuous variable
- $x_2$ is an integer variable

## Step 2: Convert the problem into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ represents 'kiwis'
- $x_2$ represents 'strawberries'

The objective function in symbolic notation is:
\[ 1x_1^2 + 3x_1x_2 + 7x_2^2 + 9x_1 + 3x_2 \]

The constraints in symbolic notation are:
- $1.97x_1 + 4.63x_2 \geq 71$
- $1.97x_1 + 4.63x_2 \leq 138$
- $8x_1^2 - 8x_2^2 \geq 0$

## 3: Express the problem in a format that can be used to generate Gurobi code
The problem can be expressed as:
```json
{
'sym_variables': [('x1', 'kiwis'), ('x2', 'strawberries')],
'objective_function': '1*x1^2 + 3*x1*x2 + 7*x2^2 + 9*x1 + 3*x2',
'constraints': [
    '1.97*x1 + 4.63*x2 >= 71',
    '1.97*x1 + 4.63*x2 <= 138',
    '8*x1^2 - 8*x2^2 >= 0',
    'x2 is an integer'
]
}
```

## 4: Generate Gurobi code for the problem
```python
import gurobi

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

    # Define the variables
    kiwis = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="kiwis", vtype=gurobi.GRB.CONTINUOUS)
    strawberries = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="strawberries", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(kiwis**2 + 3*kiwis*strawberries + 7*strawberries**2 + 9*kiwis + 3*strawberries, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(1.97*kiwis + 4.63*strawberries >= 71, name="tastiness_rating_min")
    model.addConstr(1.97*kiwis + 4.63*strawberries <= 138, name="tastiness_rating_max")
    model.addConstr(8*kiwis**2 - 8*strawberries**2 >= 0, name="tastiness_rating_diff")

    # Update the model
    model.update()

    # Solve the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Kiwis: {kiwis.varValue}")
        print(f"Strawberries: {strawberries.varValue}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has no optimal solution.")

solve_optimization_problem()
```