To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (steaks and kale salads), formulating the objective function using these variables, and then listing all constraints in terms of these variables.

Let's denote:
- \(x_0\) as the number of steaks,
- \(x_1\) as the number of kale salads.

The objective function to minimize is: \(3x_0 + 8x_1\).

Given resources/attributes are:
- Each steak has 7 grams of protein and a healthiness rating of 8.
- Each kale salad has 2 grams of protein and a healthiness rating of 1.

Constraints:
1. Total combined grams of protein from steaks and kale salads must be at least 28: \(7x_0 + 2x_1 \geq 28\).
2. The total combined healthiness rating from steaks and kale salads must be at least 20: \(8x_0 + x_1 \geq 20\).
3. Minus nine times the number of steaks plus 9 times the number of kale salads should be greater than or equal to zero: \(-9x_0 + 9x_1 \geq 0\).
4. The total combined grams of protein from steaks and kale salads should be no more than 58: \(7x_0 + 2x_1 \leq 58\).
5. The total combined healthiness rating from steaks and kale salads must be at most 27: \(8x_0 + x_1 \leq 27\).

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x0', 'steaks'), ('x1', 'kale salads')],
    'objective_function': '3*x0 + 8*x1',
    'constraints': [
        '7*x0 + 2*x1 >= 28',
        '8*x0 + x1 >= 20',
        '-9*x0 + 9*x1 >= 0',
        '7*x0 + 2*x1 <= 58',
        '8*x0 + x1 <= 27'
    ]
}
```

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

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="steaks")
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.INTEGER, name="kale_salads")

# Define the objective function
m.setObjective(3*x0 + 8*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(7*x0 + 2*x1 >= 28, "protein_min")
m.addConstr(8*x0 + x1 >= 20, "healthiness_min")
m.addConstr(-9*x0 + 9*x1 >= 0, "steaks_vs_salads")
m.addConstr(7*x0 + 2*x1 <= 58, "protein_max")
m.addConstr(8*x0 + x1 <= 27, "healthiness_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Steaks: {x0.x}")
    print(f"Kale Salads: {x1.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```