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

Given:
- Variables: `zucchini vines` and `cucumber vines`
- Resources/Attributes:
  - `r0`: resilience index
    - Upper bound: 159
    - `x0` (zucchini vines): 8
    - `x1` (cucumber vines): 15
  - `r1`: planting space
    - Upper bound: 172
    - `x0` (zucchini vines): 6
    - `x1` (cucumber vines): 1

Let's denote:
- `x0` as the number of zucchini vines
- `x1` as the number of cucumber vines

The objective function to minimize is: `2.44*x0 + 6.82*x1`

Constraints:
1. Total resilience index must be at least 66: `8*x0 + 15*x1 >= 66`
2. Total planting space must be at least 66 sq. ft: `6*x0 + x1 >= 66`
3. Eight times the number of zucchini vines minus three times the number of cucumber vines must be no less than zero: `8*x0 - 3*x1 >= 0`
4. The total combined resilience index should not exceed 159: `8*x0 + 15*x1 <= 159`
5. The total planting space occupied should not exceed 172 sq. ft, but we have a stricter constraint of 159 sq. ft from the resilience index, so we consider this implicitly satisfied if we adhere to the resilience index limit and other constraints.
6. Both `x0` and `x1` must be integers (whole numbers).

Now, let's provide the symbolic representation as requested:

```json
{
  'sym_variables': [('x0', 'zucchini vines'), ('x1', 'cucumber vines')],
  'objective_function': '2.44*x0 + 6.82*x1',
  'constraints': [
    '8*x0 + 15*x1 >= 66',
    '6*x0 + x1 >= 66',
    '8*x0 - 3*x1 >= 0',
    '8*x0 + 15*x1 <= 159'
  ]
}
```

Next, we implement this optimization problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="zucchini_vines")
x1 = m.addVar(vtype=GRB.INTEGER, name="cucumber_vines")

# Set the objective function
m.setObjective(2.44*x0 + 6.82*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(8*x0 + 15*x1 >= 66, "Resilience_Index_Lower_Bound")
m.addConstr(6*x0 + x1 >= 66, "Planting_Space_Lower_Bound")
m.addConstr(8*x0 - 3*x1 >= 0, "Vine_Relation_Constraint")
m.addConstr(8*x0 + 15*x1 <= 159, "Resilience_Index_Upper_Bound")

# Optimize the model
m.optimize()

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