## Step 1: Define the symbolic representation of the variables
The variables are ['strawberries', 'cantaloupes', 'kiwis'], which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : strawberries
- $x_1$ : cantaloupes
- $x_2$ : kiwis

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is $2.98x_0 + 3.42x_1 + 2.74x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $4x_0 + 6x_1 \geq 30$
2. $4x_0 + 6x_1 + 5x_2 \geq 30$
3. $11x_0 + 10x_1 \geq 22$
4. $11x_0 + x_2 \geq 17$
5. $11x_0 + 10x_1 + x_2 \geq 17$
6. $-2x_0 + 4x_2 \geq 0$
7. $4x_0 + 6x_1 + 5x_2 \leq 98$
8. $11x_0 + x_2 \leq 74$
9. $10x_1 + x_2 \leq 67$
10. $x_0$ is an integer
11. $x_1$ is a continuous variable
12. $x_2$ is an integer

## 5: Provide the symbolic representation in JSON format
```json
{
'sym_variables': [('x0', 'strawberries'), ('x1', 'cantaloupes'), ('x2', 'kiwis')],
'objective_function': '2.98*x0 + 3.42*x1 + 2.74*x2',
'constraints': [
    '4*x0 + 6*x1 >= 30',
    '4*x0 + 6*x1 + 5*x2 >= 30',
    '11*x0 + 10*x1 >= 22',
    '11*x0 + x2 >= 17',
    '11*x0 + 10*x1 + x2 >= 17',
    '-2*x0 + 4*x2 >= 0',
    '4*x0 + 6*x1 + 5*x2 <= 98',
    '11*x0 + x2 <= 74',
    '10*x1 + x2 <= 67'
]
}
```

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

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

# Define the variables
x0 = model.addVar(name="strawberries", vtype=gurobi.GRB.INTEGER)
x1 = model.addVar(name="cantaloupes", vtype=gurobi.GRB.CONTINUOUS)
x2 = model.addVar(name="kiwis", vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(2.98 * x0 + 3.42 * x1 + 2.74 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(4 * x0 + 6 * x1 >= 30)
model.addConstr(4 * x0 + 6 * x1 + 5 * x2 >= 30)
model.addConstr(11 * x0 + 10 * x1 >= 22)
model.addConstr(11 * x0 + x2 >= 17)
model.addConstr(11 * x0 + 10 * x1 + x2 >= 17)
model.addConstr(-2 * x0 + 4 * x2 >= 0)
model.addConstr(4 * x0 + 6 * x1 + 5 * x2 <= 98)
model.addConstr(11 * x0 + x2 <= 74)
model.addConstr(10 * x1 + x2 <= 67)

# Update the model
model.update()

# Solve the model
model.optimize()

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