To solve the given optimization problem using Gurobi, we need to translate the natural language description into a mathematical formulation and then express this in Python code using the Gurobi library.

The objective function aims to maximize the value of:
\[ 4.72 \times \text{honeypots} + 8.14 \times \text{system administrators} + 1.24 \times \text{SOC operators} \]

Subject to several constraints:

1. The total data integrity impact from all sources should respect specific bounds.
2. Each type of resource (honeypots, system administrators, SOC operators) has its own data integrity impact per unit.
3. Specific combinations of these resources have minimum and maximum total data integrity impacts.

Given:
- Honeypot's data integrity impact: 3.43
- System administrator's data integrity impact: 3.96
- SOC operator's data integrity impact: 5.3

Constraints:
- Total combined data integrity impact from honeypots and system administrators ≥ 24.
- Total combined data integrity impact from all three ≥ 19.
- Maximum total combined data integrity impact from system administrators and SOC operators = 110.
- Maximum total combined data integrity impact from all three ≤ 110.

All variables (honeypots, system administrators, SOC operators) must be whole numbers.

Here's how we can implement this in Gurobi:

```python
from gurobipy import *

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

# Define the variables
honeypots = m.addVar(vtype=GRB.INTEGER, name="honeypots")
system_administrators = m.addVar(vtype=GRB.INTEGER, name="system_administrators")
soc_operators = m.addVar(vtype=GRB.INTEGER, name="soc_operators")

# Define the objective function
m.setObjective(4.72 * honeypots + 8.14 * system_administrators + 1.24 * soc_operators, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3.43 * honeypots + 3.96 * system_administrators >= 24, name="min_honeypot_sysadmin_impact")
m.addConstr(3.43 * honeypots + 3.96 * system_administrators + 5.3 * soc_operators >= 19, name="min_total_impact")
m.addConstr(3.96 * system_administrators + 5.3 * soc_operators <= 110, name="max_sysadmin_socop_impact")
m.addConstr(3.43 * honeypots + 3.96 * system_administrators + 5.3 * soc_operators <= 110, name="max_total_impact")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Honeypots: {honeypots.x}")
    print(f"System Administrators: {system_administrators.x}")
    print(f"SOC Operators: {soc_operators.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```