To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. This involves identifying the variables, the objective function, and the constraints.

The variables in this problem are:
- `x0`: packs of paper plates
- `x1`: candles

The objective function is to minimize: `1*x0 + 5*x1`

Given the resources/attributes:
- Each pack of paper plates has a dollar value of $5 and a portability rating of 1.
- Each candle has a dollar value of $8 and a portability rating of 2.

The constraints are as follows:
1. The total value of packs of paper plates and candles must be at least $35: `5*x0 + 8*x1 >= 35`
2. The combined value constraint is already covered by the first point.
3. The total combined portability rating from packs of paper plates and candles must be at minimum 7: `1*x0 + 2*x1 >= 7`
4. This constraint is the same as the third point.
5. `-4*x0 + 9*x1 >= 0`
6. The combined value of packs of paper plates plus candles must be at most $54: `5*x0 + 8*x1 <= 54`
7. The total combined portability rating from packs of paper plates and candles must be at maximum 39: `1*x0 + 2*x1 <= 39`
8. You're limited to a whole number of packs of paper plates.
9. You are not allowed to have a fractional number of candles.

Symbolic representation:
```json
{
    'sym_variables': [('x0', 'packs of paper plates'), ('x1', 'candles')],
    'objective_function': 'minimize 1*x0 + 5*x1',
    'constraints': [
        '5*x0 + 8*x1 >= 35',
        '1*x0 + 2*x1 >= 7',
        '-4*x0 + 9*x1 >= 0',
        '5*x0 + 8*x1 <= 54',
        '1*x0 + 2*x1 <= 39'
    ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Add variables to the model
x0 = m.addVar(vtype=GRB.INTEGER, name="packs_of_paper_plates")
x1 = m.addVar(vtype=GRB.INTEGER, name="candles")

# Set the objective function
m.setObjective(1*x0 + 5*x1, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(5*x0 + 8*x1 >= 35, "total_value_min")
m.addConstr(1*x0 + 2*x1 >= 7, "portability_rating_min")
m.addConstr(-4*x0 + 9*x1 >= 0, "custom_constraint")
m.addConstr(5*x0 + 8*x1 <= 54, "total_value_max")
m.addConstr(1*x0 + 2*x1 <= 39, "portability_rating_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Packs of paper plates: {x0.x}")
    print(f"Candles: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```