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

Let's denote:
- $x_1$ as the number of basil plants,
- $x_2$ as the number of verbenas.

The objective function to minimize is given by: $4.77x_1 + 2.3x_2$.

Constraints are as follows:
1. The total combined beauty rating from basil plants and verbenas must be at least 8: $8.95x_1 + 10.05x_2 \geq 8$.
2. The same constraint is mentioned twice, so we keep it only once for simplicity.
3. $-8x_1 + 4x_2 \geq 0$.
4. The total combined beauty rating from basil plants and verbenas must not exceed 29: $8.95x_1 + 10.05x_2 \leq 29$.
5. Both $x_1$ and $x_2$ must be integers (non-fractional).

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'basil plants'), ('x2', 'verbenas')],
    'objective_function': '4.77*x1 + 2.3*x2',
    'constraints': [
        '8.95*x1 + 10.05*x2 >= 8',
        '-8*x1 + 4*x2 >= 0',
        '8.95*x1 + 10.05*x2 <= 29'
    ]
}
```

Now, let's write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(4.77*x1 + 2.3*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(8.95*x1 + 10.05*x2 >= 8, "beauty_rating_min")
m.addConstr(-8*x1 + 4*x2 >= 0, "resource_constraint")
m.addConstr(8.95*x1 + 10.05*x2 <= 29, "beauty_rating_max")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of basil plants: {x1.x}")
    print(f"Number of verbenas: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```