To solve this optimization problem, we will use the Gurobi Python library. The goal is to minimize the objective function while satisfying all given constraints.

The objective function to be minimized is: 4 times the total number of pen testers + 2 times the total number of intrusion analysts + 1 time the amount of SOC operators.

Let's denote:
- `x0` as the number of pen testers,
- `x1` as the number of intrusion analysts, and
- `x2` as the number of SOC operators.

We will first define all variables and their constraints according to the problem statement. Then we'll express these in Gurobi's Python API.

Here is how you can implement this optimization problem using Gurobi:

```python
from gurobipy import *

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

# Define the decision variables
x0 = m.addVar(vtype=GRB.INTEGER, name="pen_testers")
x1 = m.addVar(vtype=GRB.INTEGER, name="intrusion_analysts")
x2 = m.addVar(vtype=GRB.INTEGER, name="SOC_operators")

# Define the objective function
m.setObjective(4*x0 + 2*x1 + x2, GRB.MINIMIZE)

# Constraints from the problem description

# Computational load constraints
m.addConstr(9*x0 + 8*x1 + 10*x2 >= 48, name="comp_load_1")
m.addConstr(8*x1 + 10*x2 >= 46, name="comp_load_2")
m.addConstr(9*x0 + 8*x1 + 10*x2 >= 46, name="comp_load_3")

# Data accessibility impact constraints
m.addConstr(9*x0 + 21*x1 >= 23, name="data_impact_1")
m.addConstr(21*x1 + 23*x2 >= 62, name="data_impact_2")
m.addConstr(9*x0 + 23*x2 >= 21, name="data_impact_3")
m.addConstr(9*x0 + 21*x1 + 23*x2 >= 21, name="data_impact_4")

# Power consumption constraints
m.addConstr(4*x0 + 12*x2 >= 30, name="power_consumption_1")
m.addConstr(10*x1 + 12*x2 >= 25, name="power_consumption_2")
m.addConstr((4*x0 + 10*x1 + 12*x2) >= 53, name="power_consumption_3")

# Network latency constraints
m.addConstr(11*x0 + 23*x1 >= 70, name="net_latency_1")
m.addConstr(11*x0 + 21*x2 >= 73, name="net_latency_2")
m.addConstr(11*x0 + 23*x1 + 21*x2 >= 73, name="net_latency_3")

# Available bandwidth constraints
m.addConstr(21*x0 + 23*x2 >= 35, name="bandwidth_1")
m.addConstr(21*x0 + 10*x1 >= 27, name="bandwidth_2")
m.addConstr((21*x0 + 10*x1 + 23*x2) >= 30, name="bandwidth_3")

# Additional constraints
m.addConstr(x1 - 7*x2 >= 0, name="additional_constraint_1")
m.addConstr(-x0 + 3*x1 >= 0, name="additional_constraint_2")

# Upper bound constraints for computational load and power consumption
m.addConstr(9*x0 + 8*x1 + 10*x2 <= 79, name="comp_load_max")
m.addConstr((4*x0 + 10*x1) <= 100, name="power_consumption_max_1")
m.addConstr((4*x0 + 12*x2) <= 95, name="power_consumption_max_2")

# Upper bound constraints for available bandwidth
m.addConstr((10*x1 + 23*x2) <= 123, name="bandwidth_max_1")
m.addConstr((21*x0 + 23*x2) <= 103, name="bandwidth_max_2")

# Solve the model
m.optimize()

for v in m.getVars():
    print('%s %g' % (v.varName, v.x))

print('Obj: %g' % m.objVal)
```

This code defines all variables and constraints according to the problem description. After defining the objective function and constraints, it solves the model using Gurobi's `optimize` method and prints out the optimal values of the decision variables along with the objective value.

Please ensure you have the necessary permissions and environment set up to run this code with the Gurobi library installed.