To convert the given natural language description into a symbolic representation and subsequently into Gurobi code, let's break down the problem step by step.

1. **Define Symbolic Variables**:
   - Let \(x_0\) represent the number of 'light infantry companies'.
   - Let \(x_1\) represent the number of 'water purification units'.

2. **Objective Function**:
   The objective is to minimize \(2 \times x_0 + 2 \times x_1\).

3. **Constraints**:
   - Logistics footprint constraint: \(0.37x_0 + 1.05x_1 \geq 22\)
   - Offensive capability rating constraint: \(1.57x_0 + 1.74x_1 \geq 22\)
   - Another logistics footprint constraint (ensuring it's at least 22 and no more than 65 when considering all units together is implicitly covered by the first, but we add an upper bound from problem context): \(0.37x_0 + 1.05x_1 \leq 35\)
   - Upper limit on total offensive capability: \(1.57x_0 + 1.74x_1 \leq 38\)
   - Constraint on the difference between light infantry companies and water purification units: \(8x_0 - 8x_1 \geq 0\)
   - Non-negativity and integer constraints for both variables: \(x_0, x_1 \in \mathbb{Z}^+\)

Given this breakdown, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('x0', 'light infantry companies'), ('x1', 'water purification units')],
  'objective_function': '2*x0 + 2*x1',
  'constraints': [
    '0.37*x0 + 1.05*x1 >= 22',
    '1.57*x0 + 1.74*x1 >= 22',
    '0.37*x0 + 1.05*x1 <= 35',
    '1.57*x0 + 1.74*x1 <= 38',
    '8*x0 - 8*x1 >= 0'
  ]
}
```

Now, let's express this problem in Gurobi code:
```python
from gurobipy import *

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

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

# Set objective function
m.setObjective(2*x0 + 2*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(0.37*x0 + 1.05*x1 >= 22, "logistics_footprint_lower")
m.addConstr(1.57*x0 + 1.74*x1 >= 22, "offensive_capability_lower")
m.addConstr(0.37*x0 + 1.05*x1 <= 35, "logistics_footprint_upper")
m.addConstr(1.57*x0 + 1.74*x1 <= 38, "offensive_capability_upper")
m.addConstr(8*x0 - 8*x1 >= 0, "difference_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Light Infantry Companies: {x0.x}")
    print(f"Water Purification Units: {x1.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```