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

Given:
- Variables: milligrams of vitamin D, milligrams of iron
- Objective Function: Maximize 1 * (milligrams of vitamin D) + 8 * (milligrams of iron)
- Constraints:
  - Kidney support index for vitamin D is 17.
  - Kidney support index for iron is 1.
  - Total kidney support index must be at least 48.
  - Total kidney support index must not exceed 74.
  - A specific linear combination of vitamin D and iron must be non-negative (-9 * vitamin D + 1 * iron >= 0)

Let's assign symbolic variables:
- Let x1 represent milligrams of vitamin D.
- Let x2 represent milligrams of iron.

The objective function can then be written as: Maximize x1 + 8x2

Constraints in symbolic form are:
1. 17x1 + x2 >= 48 (Total kidney support index must be at least 48)
2. 17x1 + x2 <= 74 (Total kidney support index must not exceed 74)
3. -9x1 + x2 >= 0 (Specific linear combination constraint)

Now, let's represent the problem symbolically as requested:

```json
{
  'sym_variables': [('x1', 'milligrams of vitamin D'), ('x2', 'milligrams of iron')],
  'objective_function': 'Maximize x1 + 8*x2',
  'constraints': [
    '17*x1 + x2 >= 48',
    '17*x1 + x2 <= 74',
    '-9*x1 + x2 >= 0'
  ]
}
```

To solve this optimization problem using Gurobi, we will use Python. First, ensure you have Gurobi installed in your Python environment. You can install it via pip if necessary (`pip install gurobipy`).

Here is the Gurobi code to solve the optimization problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_D")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_iron")

# Set objective function
m.setObjective(x1 + 8*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(17*x1 + x2 >= 48, name="kidney_support_index_minimum")
m.addConstr(17*x1 + x2 <= 74, name="kidney_support_index_maximum")
m.addConstr(-9*x1 + x2 >= 0, name="linear_combination_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin D: {x1.x}")
    print(f"Milligrams of Iron: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```