To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the provided information.

Let's denote:
- $x_1$ as the number of packs of paper plates,
- $x_2$ as the number of cartons of milk.

The objective function to minimize is: $1.68x_1 + 9.87x_2$

Constraints are as follows:
1. Total usefulness rating should be at least 35: $16.51x_1 + 6.66x_2 \geq 35$
2. Total portability rating should be at least 34: $13.09x_1 + 14.76x_2 \geq 34$
3. Another constraint given is: $-8x_1 + 3x_2 \geq 0$
4. The total combined usefulness rating has to be 71 or less: $16.51x_1 + 6.66x_2 \leq 71$
5. The total combined portability rating has to be 42 at a maximum: $13.09x_1 + 14.76x_2 \leq 42$

Given that both $x_1$ and $x_2$ must be integers, we have additional constraints:
- $x_1$ is an integer
- $x_2$ is an integer

Thus, the symbolic representation of the problem can be summarized as follows:

```json
{
  'sym_variables': [('x1', 'packs of paper plates'), ('x2', 'cartons of milk')],
  'objective_function': '1.68*x1 + 9.87*x2',
  'constraints': [
    '16.51*x1 + 6.66*x2 >= 35',
    '13.09*x1 + 14.76*x2 >= 34',
    '-8*x1 + 3*x2 >= 0',
    '16.51*x1 + 6.66*x2 <= 71',
    '13.09*x1 + 14.76*x2 <= 42'
  ]
}
```

Now, let's implement this problem in Gurobi:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="packs_of_paper_plates")
x2 = m.addVar(vtype=GRB.INTEGER, name="cartons_of_milk")

# Set objective function
m.setObjective(1.68*x1 + 9.87*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(16.51*x1 + 6.66*x2 >= 35, "usefulness_rating_min")
m.addConstr(13.09*x1 + 14.76*x2 >= 34, "portability_rating_min")
m.addConstr(-8*x1 + 3*x2 >= 0, "additional_constraint")
m.addConstr(16.51*x1 + 6.66*x2 <= 71, "usefulness_rating_max")
m.addConstr(13.09*x1 + 14.76*x2 <= 42, "portability_rating_max")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Packs of paper plates:", x1.x)
    print("Cartons of milk:", x2.x)
    print("Objective function value:", m.objVal)
else:
    print("No optimal solution found")
```