To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints using these variables.

Let's define:
- $x_1$ as the number of verbenas,
- $x_2$ as the number of begonias.

The objective function to maximize is: $2x_1 + 4x_2$

The constraints based on the problem description are:
1. Planting space constraint for verbenas and begonias combined must be at least 8 square feet, given that each verbena requires 11 sq. in (or 0.7692 sq. ft, since 1 sq. ft = 144 sq. in) and each begonia requires 5 sq. in (or approximately 0.3476 sq. ft): $0.7692x_1 + 0.3476x_2 \geq 8$
2. The total combined resilience index from verbenas and begonias must be greater than or equal to 4: $x_1 + x_2 \geq 4$
3. Constraint on the number of verbenas and begonias related to their indices: $6x_1 - 10x_2 \geq 0$
4. The total planting space occupied by verbenas plus begonias must not exceed 39 sq. ft, considering the conversion of square inches to square feet for each plant's requirement: $0.7692x_1 + 0.3476x_2 \leq 39$
5. The total combined resilience index should be less than or equal to 15: $x_1 + x_2 \leq 15$

Given the problem statement and the defined variables, we can represent the optimization problem symbolically as follows:

```json
{
    'sym_variables': [('x1', 'verbenas'), ('x2', 'begonias')],
    'objective_function': '2*x1 + 4*x2',
    'constraints': [
        '0.7692*x1 + 0.3476*x2 >= 8',
        'x1 + x2 >= 4',
        '6*x1 - 10*x2 >= 0',
        '0.7692*x1 + 0.3476*x2 <= 39',
        'x1 + x2 <= 15'
    ]
}
```

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

```python
from gurobipy import *

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

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

# Set objective function
m.setObjective(2*x1 + 4*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(0.7692*x1 + 0.3476*x2 >= 8, "Planting_Space_Min")
m.addConstr(x1 + x2 >= 4, "Resilience_Index_Min")
m.addConstr(6*x1 - 10*x2 >= 0, "Verb_Bego_Constraint")
m.addConstr(0.7692*x1 + 0.3476*x2 <= 39, "Planting_Space_Max")
m.addConstr(x1 + x2 <= 15, "Resilience_Index_Max")

# Optimize model
m.optimize()

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